Two linear estimators for stationary invertible vector autoregressive moving average (VARMA) models in echelon form — to achieve parameter unicity (identification) — with known Kronecker indices are studied. It is shown that both estimators are consistent and asymptotically normal with...

In a finite population setting, this paper considers a partially rank ordered set (PROS) sampling design. The PROS design selects a simple random sample (SRS) of M units without replacement from a finite population and creates a partially rank ordered judgment subsets by dividing the units in...

Optimal design for parameter estimation in Gaussian process regression models with input-dependent noise is examined. The motivation stems from the area of computer experiments, where computationally demanding simulators are approximated using Gaussian process emulators to act as statistical...

In machine learning problems a learning algorithm tries to learn the input–output dependency (relationship) of a system from a training dataset. This input–output relationship is usually deformed by a random noise. From experience, simulations, and special case theories, most practitioners...

Two fast group subset selection (GSS) algorithms for the linear regression model are proposed in this paper. GSS finds the best combinations of groups up to a specified size minimising the residual sum of squares. This imposes an l0 constraint on the regression coefficients in a group context....

In this paper, a goodness of fit (gof) test for discrete random variables is studied. For the test, the empirical process gof test constructed based on the Khmaladze transformation method is considered to remove the parameter estimation effect. Further, the approach of the continuous extension...

A random subset method (RSM) with a new weighting scheme is proposed and investigated for linear regression with a large number of features. Weights of variables are defined as averages of squared values of pertaining t-statistics over fitted models with randomly chosen features. It is argued...

We develop an infinite-order extension of the HAR-RV model, denoted by HAR(∞). We show that the autocorrelation function of the model is algebraically decreasing and thus the model is a long-memory model if and only if the HAR coefficients decrease exponentially. For a finite sample, a...

The selection of an appropriate model is a fundamental step of the data analysis in small area estimation. Bias corrections to the Akaike information criterion, AIC, and to the Kullback symmetric divergence criterion, KIC, are derived for the Fay–Herriot model. Furthermore, three...

A new version of dynamic time warping for samples of observed event times that are modeled as time-warped intensity processes is introduced. The approach is developed within a framework where for each experimental unit or subject in a sample, a random number of event times or random locations...