The finite sample properties of the state space methods applied to long memory time series are analyzed through Monte Carlo simulations. The state space setup allows to introduce a novel modeling approach in the long memory framework, which directly tackles measurement errors and random level...

Although sample size calculations for testing a parameter in the Poisson regression model have been previously done, very little attention has been given to the effect of the correlation structure of the explanatory covariates on the sample size. A method to calculate the sample size for the...

The important problems of variable selection and estimation in nonparametric additive regression models for high-dimensional data are addressed. Several methods have been proposed to model nonlinear relationships when the number of covariates exceeds the number of observations by using spline...

The Laplace approximation is used to perform maximum likelihood estimation of univariate and multivariate stochastic volatility (SV) models. It is shown that the implementation of the Laplace approximation is greatly simplified by the use of a numerical technique known as automatic...

The filtering problem (or the dynamic data assimilation problem) is studied for linear and nonlinear systems with continuous state space and over discrete time steps. Filtering approaches based on the conjugate closed skewed normal probability density function are presented. This distribution...

We propose a frequency domain generalized likelihood ratio test for testing nonstationarity in time series. The test is constructed in the frequency domain by comparing the goodness of fit in the log-periodogram regression under the varying coefficient fractionally exponential models. Under such...

The use of Bayesian methodologies for solving optimal experimental design problems has increased. Many of these methods have been found to be computationally intensive for design problems that require a large number of design points. A simulation-based approach that can be used to solve optimal...

Bandwidth selection in multivariate kernel density estimation has received considerable attention. In addition to classical methods of bandwidth selection, such as plug-in and cross-validation methods, Bayesian approaches have also been previously investigated. Bayesian estimation of adaptive...

This study develops a procedure for the statistical design of the variable sampling intervals (VSI) multivariate exponentially weighted moving average (MEWMA) chart. The VSI MEWMA chart is compared with the corresponding fixed sampling interval (FSI) MEWMA chart, in terms of the steady-state...

In this paper we derive analytic formulas for electricity derivatives under assumption that electricity spot prices follow a 3-regime Markov regime-switching model with independent spikes and drops and periodic transition matrix. Since the classical derivatives pricing methodology cannot be used...