This vignette describes the use of the DCSmoothpackage and its functions. The DCSmooth package provides some tools for non and semiparametric estimation of the mean surface \(m\) of an observed sample of some function \[y(x, t) = m(x, t) + \varepsilon(x, t).\]
This package is accompanying Schäfer and Feng (2021). Boundary modification procedures for local polynomial smoothing are considered by Feng and Schäfer (2021). For a more comprehensive and detailed description of the algorithms than in section 4, please refer to these papers.
The DCSmooth contains the following functions, methods and data sets:
Functions  

set.options() 
Define options for the dcs() function. 
dcs() 
Nonparametric estimation of the expectation function of a matrix Y . Includes automatic iterative plugin bandwidth selection. 
surface.dcs() 
3dplot for the surfaces of an "dcs" object. 
sarma.sim() 
Simulate a SARMAmodel. 
sarma.est() 
Estimate the parameters of a SARMAmodel. 
sfarima.sim() 
Simulate a SFARIMAmodel. 
sfarima.est() 
Estimate the parameters of a SFARIMAmodel. 
kernel.assign() 
Assign a pointer to a kernel function. 
kernel.list() 
Print list of kernels available in the package. 
Methods/Generics  

summary.dcs() 
Summary statistics for an object of class "dcs" . 
print.dcs() 
Print an object of class "dcs" . 
plot.dcs() 
Plot method for an "dcs" object, returns contour plot. 
residuals.dcs() 
Returns the residuals of the regression from an "dcs" object. 
print.summary_dcs() 
Print an object of class "summary_dcs" , which inherits from summary.dcs() . 
print.set_options() 
Prints an object of class "dcs_options" , which inherits from set.options() 
summary.sarma() 
Summary statistics for an object of class "sarma" 
print.summary_sarma() 
Prints an object of class "summary_sarma" , which inherits from summary.sarma() 
summary.sfarima() 
Summary statistics for an object of class "sfarima" 
print.summary_sfarima() 
Prints an object of class "summary_sfarima" , which inherits from summary.sfarima() 
Data  

y.norm1 
A surface with a single gaussian peak. 
y.norm2 
A surface with two gaussian peaks. 
y.norm3 
A surface with two gaussian ridges. 
temp.nunn 
Temperatures in Nunn, CO observed in 2020 in 5 minute intervals. (Source: NOAA) 
temp.yuma 
Temperatures in Yuma, AZ observed in 2020 in 5 minute intervals. (Source: NOAA) 
wind.nunn 
Wind speed in Nunn, CO observed in 2020 in 5 minute intervals. (Source: NOAA) 
wind.yuma 
Wind speed in Yuma, AZ observed in 2020 in 5 minute intervals. (Source: NOAA) 
returns.alv 
5 minute returns of Allianz SE from 2007 to 2010 
volumes.alv 
5 minute volumes of Allianz SE from 2007 to 2010 
set.options()
This auxiliary function is used to set the options for the dcs
function. An object of class dcs_options
is created and should be used as dcs_options
 argument in the dcs
function.
Arguments of set.options()
are
"KR"
) and local polynomial regression ("LP"
), which is the default value.M
, MW
or T
. The value \(k\) is the kernel order, \(\mu\) is the smoothness degree and \(\nu\) the derivative estimated by the kernel, which must match the order of derivative drv
. For more information on the kernels see section 4.3, a list of available kernels is given in A.1. The default kernels are "MW_220"
for both dimensions."model_method"
Currently available are
"iid"
(independently identically distributed errors with variance estimation from the residuals, set as default)."sarma_sep"
(separable spatial ARMA (SARMA) process, two univariate processes in both directions are estimated via stats::arima
)."sarma_HR"
(fast estimation of an SARMA process by the HannanRissanen algorithm)."sarma_RSS
(estimation of an separable SARMA process by numerical minimization of the RSS)."sfarima_RSS"
(estimation of a separable spatial FARIMA (SFARIMA) model by numerical minimization of the RSS).The models and estimation methods are described in more detail in Section 4.4.
The option var_est with values
c("iid", "qarma", "sarma", "lm")
has been replaced by var_model. For compatibility reasons, the option can still be used inset.options
but will be converted to var_model
set.options
. The default values of these options typically depend on other options and thus are put in an ellipsis. Accepted arguments are
infl_par
), the inflation exponents (infl_exp
) and trimming parameters for stabilized estimation of the necessary derivatives (trim
). Another option to further stabilize estimation of derivatives at the boundaries is the use of a constant estimation window at the boundaries setting the logical flag const_window
to TRUE
. The default values for the IPIoptions depend partly on the regression type and error model selected and are given below. For convenience, the contents of IPI_options
can be passed directly into the ellipsis ...
of set.options()
.list(ar = c(1, 1), ma = c(1, 1))
(the default for SARMA and SFARIMA) specifying the model order, or any of c("aic", "bic", "gpac")
specifying an order selection criterion. Note that gpac
does not work under SFARIMA errors.model_order
. Is a list of the form list(ar = c(1, 1), ma = c(1, 1))
(the default).set.options()
returns an object of class "dcs_options
including the following values
drv
by \(p_k = \nu_k + 1, k = x, t\).type
(see default values for KR
and LP
below).model_order
and order_max
if available.Every argument of the
set.options
function has a default value. Hence, just usingset.options()
will produce a complete set of options for double conditional smoothing regression indcs
(which is also implemented as default options indcs
, if the argumentdcs_options
is omitted).
Default options for kernel regression (type = "KR"
) are
#> dcs_options
#> 
#> options for DCS rows cols
#> 
#> type: kernel regression
#> kernels used: MW_220 MW_220
#> derivative: 0 0
#> variance model:
#> 
#> IPI options:
#> inflation parameters 2 1
#> inflation exponents 0.5 0.5
#> trim 0.05 0.05
#> constant window width FALSE
#> 
Default options for local polynomial regression (type = "LP"
) are
#> dcs_options
#> 
#> options for DCS rows cols
#> 
#> type: local polynomial regression
#> kernel order: MW_220 MW_220
#> derivative: 0 0
#> polynomial order: 1 1
#> variance model: iid
#> 
#> IPI options:
#> inflation parameters 2 1
#> inflation exponents auto
#> trim 0.05 0.05
#> constant window width FALSE
#> 
dcs()
The dcs()
function is the main function of the package and includes IPIbandwidth selection and nonparametric smoothing of the observations Y
using the selected bandwidths. This function creates an object of class dcs
, which includes the results of the DCS procedure.
Arguments of dcs()
are
Y
has to have at least five rows and columns, however, for reliable results the size should be larger."dcs_options"
created by set.options()
. This argument is optional, if omitted, all options will be set to their default values from the set.options()
function."auto"
if bandwidth selection should be employed (the default).X
and T
which should be ordered numerical vectors whose length matches the number of rows of Y
for X
and the number of columns of Y
for T
.dcs
returns an object of class "dcs
including the following values
h = "auto
is used in dcs
, the bandwidths are optimized via the IPIalgorithm, if h
is set to fixed values, these bandwidth are used.Y
. Either obtained by IPI bandwidth selection or given as argument in dcs
.NA
, if no bandwidth selection is used.$R
. Output depends on the model specification used in dcs_options$var_model
. For var_model = "iid"
, it contains the estimated standard deviation of the residuals and an indicator for stationarity, which is true by assumption. For dcs_options$var_model = c("sarma_sep", "sarma_RSS", "sarma_HR")
, it contains the estimated model in an object of class "sarma"
including the coefficient matrices $ar
and $ma
, the standard deviation $sigma
as well as an stationarity indicator $stnry
. For dcs_options$var_model = "sfarima_RSS"
, the output is of class "sfarima"
, with similar contents as "sarma"
and the addition of the estimated long memory parameter vector $d
."dcs_options"
containing the options used in the function.NA
, if no bandwidth selection is used.NA
, if no bandwidth selection is used.surface.dcs()
This function is a convenient wrapper for the plotly::plot_ly()
function of the plotly package, for easy displaying of the considered surfaces. Direct plotting is available for any object of class "dcs"
or any numeric matrix Y
.
Arguments of surface.dcs()
are
"dcs"
, inheriting from a call to dcs()
or a numeric matrix, which is then directly passed to plotly::plot_ly()
.Y
is an object of class "dcs"
. Specifies the surface to be plotted, 1
for the original observations, 2
for the smoothed surface and 3
for the residual surface. If plot_choice
is omitted and Y
is an "dcs"
object, a choice dialogue will be prompted to the console, which asks to state one of the available options.plotly::plot_ly()
function.surface.dcs()
returns an object of class "plotly"
.
sarma.sim()
Simulation of a spatial ARMA process (SARMA). This function returns an object of class "sarma"
with attribute "subclass" = "sim"
. The simulated innovations are created from a normal distribution with specified standard deviation \(\sigma\). This function uses a burnin period for more consistent results.
Arguments of sarma.sim()
are
n_x
specifies the number of rows and n_t
the number of columns. Initially, a matrix Y'
of size \(2 n_x \times 2 n_t\) is simulated, for which simulation points with \(i \leq n_x\) or \(j \leq n_t\) are discarded (burnin period).list(ar, ma, sigma)
. The values ar
and ma
are matrices of size \((p_x + 1) \times (p_t + 1)\) respective \((q_x + 1) \times (q_t + 1)\) and contain the coefficients in ascending lag order, so that the upper left entry is equal to 1 (for lag 0 in both dimensions). The standard deviation of the iid. innovations with zero mean is sigma
, which should be a single positive number. See the examples in the application part 3.3 and the notation of SARMA models in Section 4.4.sarma.sim()
returns an object of class "sarma"
with attribute "subclass" = "sim"
including the following values:
n_x
, n_t
). The matrix \(Y\) is the lower left \(n_x \times n_t\) submatrix of the actually simulated matrix \(Y'\) of size \(2 n_x \times 2 n_t\) to avoid effects from setting the initial values (burnin period).model\$sigma
). As with Y
, the original matrix has size \(2 n_x \times 2 n_t\).sarma.est()
Estimate the parameters of an SARMA of given order. It returns an object of class "sarma"
with attribute "subclass" = "est"
. For estimation, three methods are available.
Arguments of sarma.est()
are
"HR"
), a separable model using two univariate estimations via stats::arima
("sep"
) and a separable model which minimizes the residual sum of squares (RSS) of the model ("RSS"
).list(ar = c(1, 1), ma = c(1, 1))
, where all orders should be nonnegative integers. A SARMA\(((1,1), (1,1))\) model is estimated by default, if model_order
is omitted.sarma.est()
returns an object of class "sarma"
with attribute "subclass" = "est"
including the following values:
ar
of autoregressive coefficients, the matrix ma
of moving average coefficients as well as the standard deviation of residuals sigma
.sfarima.sim()
Simulation of a (separable) spatial fractional ARIMA (SFARIMA) process. This function returns an object of class "sfarima"
with attribute "subclass" = "sim"
. The simulated innovations are created from a normal distribution with specified standard deviation \(\sigma\). This function uses a burnin period for more consistent results.
Arguments of sfarima.sim()
are
n_x
specifies the number of rows and n_t
the number of columns. Initially, a matrix Y'
of size \(2 n_x \times 2 n_t\) is simulated, for which simulation points with \(i \leq n_x\) or \(j \leq n_t\) are discarded (burnin period).list(ar, ma, d, sigma)
. The values ar
and ma
are matrices of size \((p_x + 1) \times (p_t + 1)\) respective \((q_x + 1) \times (q_t + 1)\) and containing the coefficients in ascending lag order, so that the upper left entry is equal to 1 (for lag 0 in both dimensions). The longmemory parameters \(d_x, d_t\) are stored in d
, a numerical vector of length 2, with \(0 < d_x, d_t < 0.5\). The standard deviation of the iid. innovations with zero mean is sigma
, which should be a single positive number. See the examples in the application part 3.4 and the notation of the short memory SARMA part in Section 4.4.sfarima.sim()
returns an object of class "sfarima"
with attribute "subclass" = "sim"
including the following values:
n_x
, n_t
). The matrix \(Y\) is the lower left \(n_x \times n_t\) submatrix of the actually simulated matrix \(Y'\) of size \(2 n_x \times 2 n_t\) to avoid effects from setting the initial values (burnin period).model\$sigma
). As with Y
, the original matrix has size \(2 n_x \times 2 n_t\).sfarima.est()
Estimation of an SFARIMA process. This function minimizes the residual sum of squares (RSS) to estimate the SFARIMAparameters of a given order. It returns an object of class "sfarima"
with attribute "subclass" = "est"
.
Arguments of sfarima.est()
are
list(ar = c(1, 1), ma = c(1, 1))
. All orders should be nonnegative integers. A SFARIMA\(((1,1), (1,1))\) model is estimated by default, if model_order
is omitted.sfarima.est()
returns an object of class "sfarima"
with attribute "subclass" = "est"
including the following values:
ar
of autoregressive coefficients, the matrix ma
of moving average coefficients as well as the vector d
holding the longmemory parameters and the standard deviation of residuals sigma
.kernel.assign()
This function sets an external pointer to a specified boundary kernel available in the DCSmooth package. These kernels are functions \(K(u, q)\), where \(u\) is a vector on \([q, 1]\) and \(q \in [0, 1]\). The boundary kernels are as proposed by Müller and Wang (1994), Müller (1991) and constructed via the method described in Feng and Schäfer (2021). Available types are MüllerWang (MW
), Müller (M
) and truncated kernels (T
).
Arguments of kernel.assign()
are
"X_abc"
, where X
is specifies the type (MW
, M
, T
), a
i the kernel order \(k\), b
is the degree of smoothness \(\mu\) and c
is the order of derivative \(\nu\). A list of currently usable kernel identifiers can be accessed with the function kernel.list()
.kernel.assign()
returns an object of class "function"
, which points to a precompiled kernel function.
kernel.list()
kernel.list()
prints the available identifiers for use in kernel.assign()
.
The argument of kernel.list()
is
kernel.list()
returns a list including the available kernels as character strings, if the argument is print = FALSE
.
Available kernels are
\(k\)  \(\mu\)  \(\nu\)  Truncated Kernels  Müller Kernels  MüllerWang Kernels 

2  0  0  M_200 
MW_200 

2  1  0  M_210 
MW_210 

2  2  0  T_220 
M_220 
MW_220 
3  2  1  T_321 
M_321 
MW_320 
4  2  0  T_420 
M_420 
MW_420 
4  2  1  M_421 
MW_421 

4  2  2  T_422 
M_422 
MW_422 
The DCSmooth package contains the following methods
Function  Methods/Generics available 

dcs_options 
print , summary 
dcs 
plot , print , print.summary , residuals , summary 
sarma 
print.summary , summary 
sfarima 
print.summary , summary 
This package contains three simulated example data sets and six data sets of environmental spatial time series.
Each of the three simulated example data sets is a matrix of size \(101 \times 101\) computed on \([0,1]^2\) for the following functions, where \(N(\mu, \Sigma)\) is the bivariate normal distribution with mean vector \(\mu\) and covariance matrix \(\Sigma\):
# surface.dcs(y.norm1)
# surface.dcs(y.norm2)
# surface.dcs(y.norm3)
The environmental application data sets feature the temperature and wind speed surfaces of Nunn, CO (temp.nunn
, wind.nunn
) and Yuma, AZ (temp.yuma
, wind.yuma
). The observations are taken in 2020 in 5minute intervals. The temperatures are given in Celsius and wind speed is given in m/s. All data sets consist therefore of 288 columns (the intraday observations) and 366 rows (the days). The data is taken from the U.S. Climate Reference Network database at www.ncdc.noaa.gov. (see Diamond et al., 2013)
# surface.dcs(temp.nunn)
# surface.dcs(temp.yuma)
# surface.dcs(wind.nunn)
# surface.dcs(wind.yuma)
For examples of financial applications, the return and volume data of German insurance company Allianz SE is available in the package. The data is aggregated to the 5minute level over the years 20072010, hence, the financial crisis 2008 is covered. The matrices consist of 1016 rows representing the days and 101 (returns) respective 102 (volumes) columns for the intraday 5minute intervals.
# surface.dcs(returns.alv)
# surface.dcs(volumes.alv)
The application of the package is demonstrated at the example of the simulated function y.norm1
, which represents a gaussian peak on \([0,1]^2\) with \(n_x = n_t = 101\) evaluation points. Different models are simulated and estimation using dcs
is demonstrated. Whenever default options are used, they are not explicitly used as function arguments, instead only when deviating from the defaults, the options are changed.
In order to keep the file size manageable, the
surface.dcs
commands in this vignette are commented out. Run the complete code used in section 3 of this vignette bydemo("DCS_demo", package = "DCSmooth")
, but note that it might take some time.
In order to set specific options use the set.options()
function to create an object of class "dcs_options"
.
= set.options(type = "KR", kerns = c("M_220", "M_422"), drv = c(0, 2),
opt1 var_model = "sarma_RSS",
IPI_options = list(trim = c(0.1, 0.1), infl_par = c(1, 1),
infl_exp = c(0.7, 0.7),
const_window = TRUE),
model_order = list(ar = c(1, 1), ma = c(0, 0)))
summary(opt1)
#> dcs_options
#> 
#> options for DCS rows cols
#> 
#> type: kernel regression
#> kernels used: M_220 M_422
#> derivative: 0 2
#> variance model:
#> 
#> IPI options:
#> inflation parameters 1 1
#> inflation exponents 0.7 0.7
#> trim 0.1 0.1
#> constant window width TRUE
#> 
class(opt1)
#> [1] "dcs_options"
The contents of the advanced option IPI_options
can be set directly as argument in set.options()
. Changing these options might lead to non convergent bandwidths.
= set.options(type = "KR", kerns = c("M_220", "M_422"), drv = c(0, 2),
opt2 var_model = "sarma_RSS", trim = c(0.1, 0.1),
infl_par = c(1, 1), infl_exp = c(0.7, 0.7),
const_window = TRUE,
model_order = list(ar = c(1, 1), ma = c(0, 0)))
summary(opt2)
#> dcs_options
#> 
#> options for DCS rows cols
#> 
#> type: kernel regression
#> kernels used: M_220 M_422
#> derivative: 0 2
#> variance model:
#> 
#> IPI options:
#> inflation parameters 1 1
#> inflation exponents 0.7 0.7
#> trim 0.1 0.1
#> constant window width TRUE
#> 
class(opt2)
#> [1] "dcs_options"
When using a model selection procedure, the additional option order_max
is available:
= set.options(var_model = "sarma_sep", model_order = "bic",
opt3 order_max = list(ar = c(0, 1), ma = c(2, 2)))
summary(opt3)
#> dcs_options
#> 
#> options for DCS rows cols
#> 
#> type: local polynomial regression
#> kernel order: MW_220 MW_220
#> derivative: 0 0
#> polynomial order: 1 1
#> variance model: sarma_sep
#> 
#> IPI options:
#> inflation parameters 2 1
#> inflation exponents auto
#> trim 0.05 0.05
#> constant window width FALSE
#> 
The example data set is simulated by using iid. errors:
# surface.dcs(y.norm1)
= y.norm1 + matrix(rnorm(101^2), nrow = 101, ncol = 101)
y_iid # surface.dcs(y_iid)
While local linear regression has some clear advantages over kernel regression, kernel regression is the faster method.
= set.options(type = "KR")
opt_iid_KR = dcs(y_iid, opt_iid_KR)
dcs_iid_KR
# print results
dcs_iid_KR#> dcs
#> 
#> DCS with automatic bandwidth selection:
#> 
#> Selected Bandwidths:
#> h_x: 0.18875
#> h_t: 0.19282
#> Variance Factor:
#> c_f: 1
#> 
# print options used for DCS procedure
$dcs_options
dcs_iid_KR#> dcs_options
#> 
#> options for DCS rows cols
#> 
#> type: kernel regression
#> kernels used: MW_220 MW_220
#> derivative: 0 0
#> variance model: iid
#> 
# plot regression surface
# surface.dcs(dcs_iid_KR, plot_choice = 2)
The summary of the "dcs"
object provides some more detailed information:
summary(dcs_iid_KR)
#> summary_dcs
#> 
#> DCS with automatic bandwidth selection:
#> 
#> Results of kernel regression:
#> Estimated Bandwidths: h_x: 0.1888
#> h_t: 0.1928
#> Variance Factor: c_f: 1
#> Iterations: 4
#> Time used (seconds): 0.03123
#> 
#> Variance Model: iid
#> 
#> sigma: 0.9968943
#> stationary: TRUE
#> 
#> See used parameter with "$dcs_options".
This is the default method, specification of options is not necessary. Note that local polynomial regression requires the bandwidth to cover at least the number of observations of the polynomial order plus one. For small bandwidths or too few observation points in one dimension, local polynomial regression might fail (“Bandwidth h must be larger for local polynomial regression.”). It is suggested to use kernel regression in this case.
= dcs(y_iid)
dcs_LP_iid
dcs_LP_iid#> dcs
#> 
#> DCS with automatic bandwidth selection:
#> 
#> Selected Bandwidths:
#> h_x: 0.1941
#> h_t: 0.20778
#> Variance Factor:
#> c_f: 1.0003
#> 
summary(dcs_LP_iid)
#> summary_dcs
#> 
#> DCS with automatic bandwidth selection:
#> 
#> Results of local polynomial regression:
#> Estimated Bandwidths: h_x: 0.1941
#> h_t: 0.2078
#> Variance Factor: c_f: 1
#> Iterations: 4
#> Time used (seconds): 0.04686
#> 
#> Variance Model: iid
#> 
#> sigma: 0.9970611
#> stationary: TRUE
#> 
#> See used parameter with "$dcs_options".
# plot regression surface
# surface.dcs(dcs_LP_iid, plot_choice = 2)
A matrix containing innovations following a SARMA\(((p_x, p_t), (q_x, q_t))\) process can be obtained by using the sarma.sim()
function. We use the following SARMA\(((1, 1), (1, 1))\)process as example: \[
AR = \begin{pmatrix} 1 & 0.4 \\ 0.3 & 0.12 \end{pmatrix},
\quad
MA = \begin{pmatrix} 1 & 0.5 \\ 0.2 & 0.1 \end{pmatrix}
\quad \text{ and } \quad
\sigma^2 = 0.25
\]
= matrix(c(1, 0.3, 0.4, 0.12), nrow = 2, ncol = 2)
ar_mat = matrix(c(1, 0.2, 0.5, 0.1), nrow = 2, ncol = 2)
ma_mat = sqrt(0.25)
sigma = list(ar = ar_mat, ma = ma_mat, sigma = sigma)
model_list = sarma.sim(n_x = 101, n_t = 101, model = model_list)
sim_sarma
# SARMA observations
= y.norm1 + sim_sarma$Y
y_sarma # surface.dcs(y_sarma)
Estimation of an SARMA process for a given order is implemented via the sarma.est()
function (note that the simulated matrix can be accessed via $Y
):
= sarma.est(sim_sarma$Y, method = "HR",
est_sarma model_order = list(ar = c(1, 1), ma = c(1, 1)))
summary(est_sarma)
#> 
#> Estimation of SARMA((1,1),(1,1))
#> 
#> sigma: 0.5008
#> stationary: TRUE
#> ar:
#> lag 0 lag 1
#> lag 0 1.0000 0.4042
#> lag 1 0.3051 0.1291
#>
#> ma:
#> lag 0 lag 1
#> lag 0 1.0000 0.49690
#> lag 1 0.1804 0.07785
The dcs()
command is used with the default SARMA\(((1, 1), (1, 1))\) model (correctly specified) and with an SARMA\(((1, 1), (0, 0))\) (i.e. SAR\((1, 1)\)) model. The chosen estimation procedure is "sep"
:
# SARMA((1, 1), (1, 1))
= set.options(var_model = "sarma_sep")
opt_sarma_1 = dcs(y_sarma, opt_sarma_1)
dcs_sarma_1
summary(dcs_sarma_1$var_est)
#> 
#> Estimation of SARMA((1,1),(1,1))
#> 
#> sigma: 0.5441
#> stationary: TRUE
#> ar:
#> lag 0 lag 1
#> lag 0 1.0000 0.5003
#> lag 1 0.5246 0.2624
#>
#> ma:
#> lag 0 lag 1
#> lag 0 1.0000 0.47000
#> lag 1 0.1524 0.07165
# SARMA((1, 1), (0, 0))
# Use "sarma_HR" as it includes fast YuleWalker estimation
= set.options(var_model = "sarma_HR",
opt_sarma_2 model_order = list(ar = c(1, 1), ma = c(0, 0)))
= dcs(y_sarma, opt_sarma_2)
dcs_sarma_2
summary(dcs_sarma_2$var_est)
#> 
#> Estimation of SARMA((1,1),(0,0))
#> 
#> sigma: 0.5331
#> stationary: TRUE
#> ar:
#> lag 0 lag 1
#> lag 0 1.000 0.63580
#> lag 1 0.176 0.09775
#>
#> ma:
#> lag 0
#> lag 0 1
Automated order selection is used with model_order = c("aic", "bic", "gpac")
in set.options()
. The first one minimizes the AIC, the second one the BIC and the third uses a generalized partial autocorrelation function for order selection (not available for SFARIMA estimation). Order selection for large data sets is slowly in general, however, the "gpac"
might be slightly faster than the other two. If automatic order selection is used, the argument order_max
sets the maximum orders to be tested in the same way as model_order
is usually defined as a list.
# BIC
= set.options(var_model = "sarma_HR", model_order = "bic",
opt_sarma_3 order_max = list(ar = c(2, 2), ma = c(2, 2)))
= dcs(y_sarma, opt_sarma_3)
dcs_sarma_3
summary(dcs_sarma_3$var_est)
#> 
#> Estimation of SARMA((1,1),(1,1))
#> 
#> sigma: 0.4997
#> stationary: TRUE
#> ar:
#> lag 0 lag 1
#> lag 0 1.0000 0.4031
#> lag 1 0.3083 0.1271
#>
#> ma:
#> lag 0 lag 1
#> lag 0 1.0000 0.50190
#> lag 1 0.1879 0.07439
# gpac
= set.options(var_model = "sarma_HR", model_order = "gpac",
opt_sarma_4 order_max = list(ar = c(2, 2), ma = c(2, 2)))
= dcs(y_sarma, opt_sarma_4)
dcs_sarma_4
summary(dcs_sarma_4$var_est)
#> 
#> Estimation of SARMA((2,2),(2,0))
#> 
#> sigma: 0.5061
#> stationary: TRUE
#> ar:
#> lag 0 lag 1 lag 2
#> lag 0 1.00000 0.85200 0.291300
#> lag 1 0.46200 0.14640 0.051140
#> lag 2 0.01744 0.02795 0.009702
#>
#> ma:
#> lag 0
#> lag 0 1.00000
#> lag 1 0.33880
#> lag 2 0.01287
This package includes a bandwidth selection algorithm when the errors \(\varepsilon(x, t)\) follow an SFARIMA\(((p_x, p_t), (q_x, q_t))\) process with long memory. Order selection for SFARIMA models works exactly as in the SARMA case. We use the same SARMA model as in 3.3 with longmemory parameters \(d = (0.3, 0.1)\):
= matrix(c(1, 0.3, 0.4, 0.12), nrow = 2, ncol = 2)
ar_mat = matrix(c(1, 0.2, 0.5, 0.1), nrow = 2, ncol = 2)
ma_mat = c(0.3, 0.1)
d = sqrt(0.25)
sigma
= list(ar = ar_mat, ma = ma_mat, d = d, sigma = sigma)
model_list = sfarima.sim(n_x = 101, n_t = 101, model = model_list)
sim_sfarima
# SFARIMA surface observations
= y.norm1 + sim_sfarima$Y
y_sfarima
# surface.dcs(y_sfarima)
= set.options(var_model = "sfarima_RSS")
opt_sfarima = dcs(y_sfarima, opt_sfarima)
dcs_sfarima
summary(dcs_sfarima$var_est)
#> 
#> Estimation of SFARIMA((1,1),(1,1))
#> 
#> d: 0.3146 0.09794
#> SD (sigma): 0.4978
#> stationary: TRUE
#> ar:
#> lag 0 lag 1
#> lag 0 1.0000 0.38940
#> lag 1 0.1575 0.06132
#>
#> ma:
#> lag 0 lag 1
#> lag 0 1.00000 0.50950
#> lag 1 0.07279 0.03708
Local polynomial estimation is suitable for estimation of derivatives of a function or a surface. While estimation of derivatives works as well under dependent errors, the example uses the iid. model from 3.2. Derivatives can be computed for any derivative vector drv
, if the values are nonnegative and an appropriate kernel function is chosen (such that the derivative order of the kernel matches the derivative order in drv
). Note that the order of the polynomials for the \(\nu\)th derivative is chosen to be \(p_i = \nu_i + 1, i = x, t\). As bandwidths increase with the order of the derivatives, the bandwidth might be large for higher derivative orders.
The estimator for \(m^{(1, 0)}(x, t)\) is
= set.options(drv = c(1, 0), kerns = c("MW_321", "MW_220"))
opt_drv_1 $IPI_options$trim = c(0.1, 0.1)
opt_drv_1= dcs(y_iid, opt_drv_1)
dcs_drv_1
dcs_drv_1#> dcs
#> 
#> DCS with automatic bandwidth selection:
#> 
#> Selected Bandwidths:
#> h_x: 0.24868
#> h_t: 0.41024
#> Variance Factor:
#> c_f: 1.002
#> 
# surface.dcs(dcs_drv_1, trim = c(0.1, 0.1), plot_choice = 2)
The estimator for \(m^{(0, 2)}(x, t)\) is given by
= set.options(drv = c(0, 2), kerns = c("MW_220", "MW_422"))
opt_drv_2 $IPI_options$trim = c(0.1, 0.1)
opt_drv_2
= dcs(y_iid, opt_drv_2)
dcs_drv_2
dcs_drv_2#> dcs
#> 
#> DCS with automatic bandwidth selection:
#> 
#> Selected Bandwidths:
#> h_x: 0.28678
#> h_t: 0.37157
#> Variance Factor:
#> c_f: 1.002
#> 
# surface.dcs(dcs_drv_2, trim = c(0.1, 0.1), plot_choice = 2)
The double conditional smoothing (DCS, see Feng (2013)) is a spatial smoothing technique which effectively reduces the twodimensional estimation to two onedimensional estimation procedures. The DCS is defined for kernel regression as well as for local polynomial regression.
Classical bivariate (and multivariate) regression has been considered e.g. by Herrmann et al. (1995) (kernel regression) and Ruppert and Wand (1994) (local polynomial regression). The DCS provides now a faster and, especially for equidistant data, more efficient smoothing scheme, which leads to reduced computation time. For the DCS procedure implemented in this package, consider a \((n_x \times n_t)\)matrix \(\mathbf{Y}\) of nonempty observations \(u_{i,j}\) and equidistant covariates \(X\), \(T\) on \([0,1]\), where \(X\) has length \(n_x\) and \(T\) has length \(n_t\). The model is then \[ y_{i,j} = m(x_i, t_j) + \varepsilon_{i,j} \] where \(m(x, t)\) is the mean or trend function, \(x_i \in X\), \(t_j \in T\) and \(\varepsilon\) is a random error function with zero mean. The model in matrix form is \(\mathbf{Y} = \mathbf{M}_1 + \mathbf{E}\) at the observation points.
The main assumption of the DCS is that of product kernels, i.e. the weights in the respective methods are constructed by \(K(u,v) = K_1(u) K_2(v)\). Now, a two stage smoother can be constructed by either the kernel weights directly (kernel regression) or by using locally weighted regression with kernels \(K_1\), \(K_2\), in any case, the weights are called \(\mathbf{W}_x\) and \(\mathbf{W}_t\). The DCS procedure implemented in DCSmooth smoothes over rows (conditioning on \(X\)) first and then over columns (conditioning on \(T\)), although switching the smoothing order is exactly equivalent. Hence, the DCS is given by the following equations: \[ \begin{align*} \mathbf{\widehat{M}}_0[ \ , j] &= \mathbf{Y} \cdot \mathbf{W}_t[ \ , j] \\ \mathbf{\widehat{M}}_1[i, \ ] &= \mathbf{W}_x [i, \ ] \cdot \mathbf{\widehat{M}}_0 \end{align*} \]
The bandwidth vector \(h = (h_x, h_t)\) is selected via an iterative plugin (IPI) algorithm (Gasser et al., 1991). The IPI selects the optimal bandwidths by minimizing the mean integrated squared error (MISE) of the estimator. As the MISE includes derivatives of the regression surface \(m(x, t)\), auxiliary bandwidths for estimation of these derivatives are calculated via an inflation method. These inflation method connects the bandwidths of \(m(x, t)\) with that of a derivative \(m^{(\nu_x, \nu_t)}(x, t)\) by \[ \widetilde{h}_k = c_k \cdot h_k^{\alpha}, \quad k = x, t \] and is called exponential inflation method (EIM). The values of \(c_k\) are chosen on simulations, that of \(alpha\) are subject to the derivative of interest. The IPI now starts with an initial bandwidth \(h_0\) (chosen to be \(h_0 = (0.1, 0.1)\)) and calculates in each step \(s\) the auxiliary bandwidths \(\widetilde{h}_{k,s}\) from \(h_{s1}\) and \(h_s\) from the smoothed derivative surfaces using \(\widetilde{h}_{k,s}\). The iteration process finishes until a certain threshold is reached.
In kernel regression, the boundary problem exists, which leads to biased estimated at the boundaries of the regression surface. This problem can (partially) be solved by means of suitable boundary kernels as introduced by Müller (1991) and Müller and Wang (1994). These boundary kernels differ in their degrees of smoothness and hence lead to different estimation results at the boundaries. However, all kernels are similar to the classical kernels in the interior region of the regression.
Following Feng and Schäfer (2021), a boundary modification is also defined for local polynomial regression. In the DCSmooth package, the local polynomial regression is always with boundary modification weights. Kernel types available (either for kernel regression or local polynomial regression) are Müllertype, MüllerWangtype and truncated kernels, denoted by M
, MW
and T
. In most applications, the MüllerWang type are the preferred weighting functions.
For observations \(X = x_i\), \(x_i, i = 1, \dots, n_x\) and a given bandwidth \(h\), define \(u_r = \frac{x_r  X}{h} \in [1, q]\). A (left) boundary kernel function \(K^l_q(u)\) of order \((k, \mu, \nu)\) is defined on \([1, q]\), for \(q \in [0,1]\) and has the following properties \[
\int_{1}^q u^j K^l_q(u) \text{d} u = \begin{cases}
0 & \text{ for } 0 \leq j < k, j \neq \nu\\
(1)^\nu \nu! & \text{ for } j = \nu\\
\beta \neq 0 & \text{ for } j = k
\end{cases}
\] The corresponding right boundary kernels can be calculated by \(K^r_q(u) = (1)\nu K_q(u)\). The boundary kernels assigned by kernel.assign()
are left boundary kernels.
The SARMA process \(\varepsilon_{i,j}\) is given by the following equations: \[
\phi(B_1, B_2)\varepsilon_{i,j} = \psi(B_1, B_2)\eta_{i,j},\\
\] where the lag operators are \(B_1 \varepsilon_{i,j} = \varepsilon_{i1, j}\) and \(B_2 \varepsilon_{i,j} = \varepsilon_{i,j1}\), \(\xi \underset{iid.}{\sim} \mathcal{N}(0,\sigma^2)\) and \[
\phi(z_1, z_2) = \sum_{m = 0}^{p_1} \sum_{n = 0}^{p_2} \phi_{m, n} z_1^m z_2^n, \ \psi(z_1, z_2) = \sum_{m = 0}^{q_1} \sum_{n = 0}^{q_2} \psi_{m, n} z_1^m z_2^n.
\] The coefficients \(\psi_{m,n}\) and \(\phi_{m,n}\) are written in matrix form \[
\begin{align*}
\boldsymbol{\phi} = \begin{pmatrix}
\phi_{0,0} & \dots & \phi_{0, p_2} \\
\vdots & \ddots & \\
\phi_{p_1, 0} & & \phi_{p_1, p_2}
\end{pmatrix} \ \text{ and } \ \boldsymbol{\psi} = \begin{pmatrix}
\psi_{0, 0} & \dots & \psi_{0, q_2} \\
\vdots & \ddots & \\
\psi_{q_1, 0} & & \psi_{q_1, q_2}
\end{pmatrix},
\end{align*}
\] where \(\Phi\) is the ARpart ($var_model$ar
) and \(\Psi\) is the MApart ($var_model$ma
). The example from 3.3, \[
\begin{align}
\boldsymbol{\phi} = \begin{pmatrix} 1 & 0.4 \\ 0.3 & 0.12 \end{pmatrix}
\ \text{ and } \
\boldsymbol{\psi} = \begin{pmatrix} 1 & 0.2 \\ 0.5 & 0.1 \end{pmatrix}
\end{align},
\] would then reduce to the process \[
\varepsilon_{i,j} = 0.4\varepsilon_{i,j1}  0.3 \varepsilon_{i,j} + 0.2\varepsilon_{i1,j1} + 0.2 \xi_{i,j1} + 0.2 \xi_{i1,j} 0.5 \xi_{i1,j1} + \xi_{i,j}.
\] Note that this process can be written as product of two univariate processes in the sense that \[
\phi_1(B_1)\phi_2(B_2)^T \varepsilon_{i,j} = \psi_1(B_1) \psi_2(B_2)^T \eta_{i,j},\\
\] with \[
\phi_1 = \begin{pmatrix} 1 \\ 0.3 \end{pmatrix}, \quad \phi_2 = \begin{pmatrix} 1 \\ 0.4 \end{pmatrix}, \quad \psi_1 = \begin{pmatrix} 1 \\ 0.5 \end{pmatrix}, \quad \psi_2 = \begin{pmatrix} 1 \\ 0.2 \end{pmatrix}.
\] Hence, these process forms a separable SARMA. Estimation of separable SARMA models can be reduced to estimation of univariate ARMA models.
For estimation of SARMA models, three methods are implemented in DCSmooth:
This method is only available under the assumption of a separable model. Define two univariate time series \[ \varepsilon_{1,r} = \{\varepsilon_1\}_r = \{\varepsilon_{i,j}\}_{i + n_t (j  1)}, \quad \varepsilon_{2,s} = \{\varepsilon_2\}_s = \{\varepsilon_{i,j}\}_{j + n_x * (i  1)} \] for \(r,s = 1, \dots, n_x \cdot n_t\), \(i = 1, \dots, n_x\), \(j = 1, \dots, n_t\). The parameters \(\phi_1, \psi_1\) of \(\varepsilon_{1,r}\) and \(\phi_2, \psi_2\) of \(\varepsilon_{2,s}\) can then be estimated by wellknown maximum likelihood estimators.
The SARMA model can be rewritten as \[ \eta_{i,j} = \psi(B_1, B_2)^{1}\phi(B_1, B_2)\varepsilon_{i,j},\\ \] which allows for an \(AR(\infty)\)representation of the SARMA model \[ \eta_{i,j} = \sum_{r,s = 0}^{\infty} \theta^{AR}_{r,s} \varepsilon_{ir, sj} \]
From this, we can define the residual sum of squares (RSS) and get an estimate for the vector of coefficients \(\theta = c(\phi_{1,0}, \phi_{0,1}, \dots, \psi_{1,0}, \psi_{0,1}, \dots\) by \[ \widehat{\theta} = \arg \min RSS \approx \arg \min \sum_{i,j = 0}^{\infty} \eta_{i,j}^2 \]
Calculation of the $AR() representation of an SARMA model is difficult for a general SARMA but for a separable SARMA, the known univariate formulas hold.
These procedure can be directly used for SFARIMA models, if the long memory parameter \(d\) is included in \(\theta\) (see Beran et al. (2009)).
The previously defined estimation methods require numeric optimization of some quantities and hence take more time for calculation on a computer. The HannanRissanen algorithm (Hannan and Rissanen (1982)) provides a much faster estimation procedure. An extension to SARMA models is straightforward:
The main idea HannanRissanen algorithm is to use a highorder SAR auxiliary model for initial estimation of the non observable innovations sequence. Then, a linear regression model is applied, which yields the SARMAparameters from the observations and estimated innovations.
Let \(\{\varepsilon\}_{i,j}\) be the the ordered observations of the \(SARMA((p_x, p_t), (q_x, q_t))\) process \(\varepsilon(x, t)\) with \(i = 1, \dots, n_x, j = 1, \dots, n_t\). The SARMA parameters \(\mathbf{\phi, \mathbf{\psi}\) are then estimated by the modified HannanRissanen algorithm: 1. Obtain the auxiliary residuals \(\widetilde{\eta}_{i,j}\) by fitting a highorder autoregressive model with \((\widetilde{p}_x, \widetilde{p}_t) \geq (p_x, p_t)\) to the observations: \[ \begin{align} \widetilde{\eta}_{i,j} = \begin{cases} \sum_{m = 0}^{\widetilde{p}_1} \sum_{n = 0}^{\widetilde{p}_2} \widetilde{\phi}_{m,n} \varepsilon_{i  m, j  n}, & \widetilde{p}_1 < i \leq n_1, \widetilde{p}_2 < j \leq n_2\\ 0, &1 \leq i \leq \widetilde{p}_1, 1 \leq j \leq \widetilde{p}_2 \end{cases} \end{align} \] where \(\widetilde{\phi}_{m,n}\) is estimated by the YuleWalker equations and \(\phi_{0,0} = 1\). 2. Obtain \(\widehat{\phi}_{m,n}\) and \(\widehat{\psi}_{m,n}\) and the estimated innovations \(\widehat{\eta}_{i,j}\) by linear regression from \[ \begin{align} \varepsilon_{i,j} = \sum_{\substack{m=0\\m \neq n = 0}}^{p_1} \sum_{\substack{n=0\\n \neq m = 0}}^{p_2} \widehat{\phi}_{m,n} \varepsilon_{im,jn} + \sum_{\substack{m=0\\m \neq n = 0}}^{q_1} \sum_{\substack{n=0\\n \neq m = 0}}^{q_2} \widehat{\psi}_{m,n} \widetilde{\xi}_{im,jn} + \widehat{\xi}_{i,j} \end{align} \] The resulting coefficients \(\widehat{\mathbf{\phi}}\), \(\widehat{\mathbf{\psi}}\) are then the estimates for the parameters.
The autocovariance function of the SARMAprocess is \(\gamma(s,t) = \mathbb{E}(\varepsilon_{i,j} \varepsilon_{i+s, j+t})\). For \(\widetilde{p}_x, \widetilde{p}_t\), the spatial YuleWalker equation for the \(SAR(\widetilde{p}_x, \widetilde{p}_t)\) approximation of the SARMA is then \[ \begin{align} \mathbf{\Gamma} \ \left( \text{vec} (\widetilde{\boldsymbol{\phi}}) \setminus \{ \widetilde{\phi}_{0,0}\} \right) = \gamma \end{align} \] where \(\mathbf{\Gamma}\) is the autocovariance matrix and \(\gamma\) the vector of autocovariances when using the following notation: \[ \begin{align} \mathbf{\Gamma} &= \begin{pmatrix} \gamma(0,0) & \gamma(1,0) & \dots & \gamma(\widetilde{p}_1,0) & \gamma(2,0) & \dots & \gamma(\widetilde{p}_1  1, \widetilde{p}_2)\\ \gamma(1,0) & \gamma(0,0) & & & & & \gamma(\widetilde{p}_1  2, \widetilde{p}_2)\\ \vdots & & \ddots & & & & \vdots \\ \gamma(\widetilde{p}_1  1, \widetilde{p}_2) & & \dots & & & & \gamma(0,0) \end{pmatrix} \\ \gamma &= \left(\gamma(0,1), \gamma(0,2), \dots, \gamma(\widetilde{p}_1, 0), \dots, \gamma(\widetilde{p}_1, \widetilde{p}_2) \right)^T. \end{align} \]