This paper focuses on the problem of maximum likelihood estimation in linear mixed-effects models where outliers or unduly large observations are present in clustered or longitudinal data. Multivariate t distributions are often imposed on either random effects and/or random errors to incorporate outliers. A powerful algorithm of maximum by parts (MBP) proposed by Song, Fan and Kalbfleisch (2005...

Information and adequate data on intensity–duration–frequency of rainfall are regularly required for a variation hydrologic, environmental hydraulic applications. This paper presents intensity-duration-frequency equations Makurdi, Nigeria. Rainfall intensities from Makurdi were used to establish empirically derived constants about thirteen different equations. These evaluated statistically usin...

There exists an essential difference between the correct Auto Regressive (AR) model and the optimal ARmodel. We try to find an optimal model balancing between flexibility, using many AR-parameters, and low variance, using only a few AR-parameters. We select an optimal ARparameter configuration consisting of zero and non-zero parameters given a maximum AR-order. This optimal configuration will b...

BACKGROUND Many instruments exist to assess mental disorders and anxiety, such as the hospital anxiety and depression scale (HADS). Nothing has been evaluated on the HADS factor structure for use with orthopedic trauma patients. The aim of this study was to validate the underlying structure of the HADS. Specifically, we sought to understand which of the factor structures found in the literature...

This paper presents an important application of a novel information theoretic order estimation method, minimum description complexity (MDC). The selection of optimum number of poles and zeros in identification of LTI systems based on observed data is accomplished by MDC. The comparison of MDC with important existing order estimation methods, MDL and AIC, is provided.

The purpose of this research is to investigate the possibility of using aspects of model selection theory to overcome both a logical problem and an epistemic problem that prevents progress towards the truth to be measured while maintaining a realist approach to science. Karl Popper began such an investigation into the problem of progress in 1963 with an idea of verisimilitude, but his attempts ...

Contrasting “geometric fitting”, for which the noise level is taken as the asymptotic variable, with “statistical inference”, for which the number of observations is taken as the asymptotic variable, we give a new definition of the “geometric AIC” and the “geometric MDL” as the counterparts of Akaike’s AIC and Rissanen’s MDL. We discuss various theoretical and practical problems that emerge fro...

A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include K-fold cross-validation (K-CV), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Though these methods work well for low-dimensional problems, they are not suitable in high dimensional settings. In th...

Procedures such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL), and bootstrap information criterion have been developed in the statistical literature for model selection. Most of these methods use estimation of bias. This bias, which is inevitable in model selection problems, arises from estimating the distance between an unknown tr...