In urban and regional studies, spatial autoregressive models are widely employed to capture spatial patterns and a dependence structure in data. While numerous variable selection techniques based on the likelihood principle with favorable theoretical properties have been proposed, the practical...

We consider a threshold regression model in a long-memory setting. A method for estimating the threshold parameter is proposed. Asymptotic results are derived. The asymptotic rate of convergence turns out to be slower than under weak dependence, but approaches the usual fast rate of Op(n-1)when...

We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in models with...

We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in models with...

Optimal experimental designs are used in chemical engineering to obtain precise mathematical models. The optimal design consists of design points with a maximal amount of information and thus lead to more precise models than statistical designs. In general, the optimal design depends on an...

This paper presents a comprehensive simulation study on estimating parameters for the popular Heston stochastic volatility model. Leveraging high-frequency data, we explore, in a data-science type exercise, various spot-volatility estimation and sampling techniques, improving existing methods to...

This paper proposes robust estimators of the variogram, a statistical tool that is commonly used in geostatistics to capture the spatial dependence structure of data. The new estimators are based on the highly robust minimum covariance determinant estimator and estimate the directional variogram...

Finding the eigenvalues connected to the covariance operator of a centered Hilbert-space valued Gaussian process is genuinely considered a hard problem in several mathematical disciplines. In Statistics this problem arises for instance in the asymptotic null distribution of goodness-of-fit test...

Optimal experimental designs are used in chemical engineering to obtain precise mathematical models. The optimal design consists of design points with a maximal amount of information and thus lead to more precise models than statistical designs. In general, the optimal design depends on an...

This paper considers the classical simultaneous inference problem of testing several means in a general correlated framework. We establish an upper bound on the family-wise error rate(FWER) of Bonferroni's procedure for equicorrelated test statistics (under general distributional assumptions)....