Mixed models and unbalanced data: wherefrom,whereat and whereto?

A brief history of the early years (1820-1947) of random effects models and the estimation of variance components is followed by a personal evaluation of M.L, REML and MINQUE estimation. A method is suggested for combining ML estimator obtained from subsets of a large data set, and comments are made on the need for simulation studies to assess the degree of approximation in using asymptotic properties of ML-type estimators as if they were exact for finite-sized unbalanced data sets.     

Linked Data: What Is It and What Can It Do?

There has been a lot of talk, discussion (and even buzz) on the Internet and in the library world recently on the whole issue of linked data and what it may mean, accomplish, and even change in the Internet and library worlds. This article will attempt to clarify what linked data is, some of the issues surrounding it, and explore some possible implications.     

Linked Data: What Is It and What Can It Do?

Abstract

There has been a lot of talk, discussion (and even buzz) on the Internet and in the library world recently on the whole issue of linked data and what it may mean, accomplish, and even change in the Internet and library worlds. This article will attempt to clarify what linked data is, some of the issues surrounding it, and explore some possible implications.     

Markov-switching BILINEAR − GARCH models: Structure and estimation

Markov-switching (MS) models are becoming increasingly popular as efficient tools of modeling various phenomena in different disciplines, in particular for non Gaussian time series. In this articlept", we propose a broad class of Markov-switching BILINEAR–GARCH processes (MS ? BLGARCH hereafter) obtained by adding to a MS ? GARCH model one or more interaction components between the observed series and its volatility process. This parameterization offers remarkably rich dynamics and complex behavior for modeling and forecasting financial time-series data which exhibit structural changes. In these models, the parameters of conditional variance are allowed to vary according to some latent time-homogeneous Markov chain with finite state space or “regimes.” The main aim of this new model is to capture asymmetric and hence purported to be able to capture leverage effect characterized by the negativity of the correlation between returns shocks and subsequent shocks in volatility patterns in different regimes. So, first, some basic structural properties of this new model including sufficient conditions ensuring the existence of stationary, causal, ergodic solutions, and moments properties are given. Second, since the second-order structure provides a useful information to identify an appropriate time-series model, we derive the expression of the covariance function of for MS ? BLGARCH and for its powers. As a consequence, we find that the second (resp. higher)-order structure is similar to some linear processes, and hence MS ? BLGARCH (resp. its powers) admit an ARMA representation. This finding allows us for parameter estimation via GMM procedure proved by a Monte Carlo study and applied to foreign exchange rate of the Algerian Dinar against the single European currency.     

Cross-validation: What is it and how is it used in regression?

The purpose of this article is to explain cross-validation and describe its use in regression. Because replicability analyses are not typically employed in studies, this is a topic with which many researchers may not be familiar. As a result, researchers may not understand how to conduct cross-validation in order to evaluate the replicability of their data. This article not only explains the purpose of cross-validation, but also uses the widely available Holzinger and Swineford (1939 Holzinger, K.J., Swineford, F. (1939). A Study in Factor Analysis: The Stability of a Bi-Factor Solution. Chicago, IL: University of Chicago. Available at: http://people.cehd.tamu.edu/～bthompson/datasets.htm [Google Scholar]) dataset as a heuristic example to concretely demonstrate its use. By incorporating multiple tables and examples of SPSS syntax and output, the reader is provided with additional visual examples in order to further clarify the steps involved in conducting cross-validation. A brief discussion of the limitations of cross-validation is also included. After reading this article, the reader should have a clear understanding of cross-validation, including when it is appropriate to use, and how it can be used to evaluate replicability in regression.     

Number of predictors and multicollinearity: What are their effects on error and bias in regression?

The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems.     

Journal Access Initiatives: Where Are They? Why the World Health Organization? and What Is HINARI?

Abstract

The move from print to online journal publishing has allowed the proliferation of journal access programs aimed at poor countries. These programs offer access to online journals on very favorable terms to developing country institutions and readers and are based on the premise that developing world scientists can contribute significantly to ameliorating the conditions of life in their countries. The authors give a brief overview of the environment in which these programs emerged, discuss different orientations of the major programs, examine the case of the Health InterNetwork Access to Research Initiative (HINARI), consider why the World Health Organization (WHO) runs a journal access program for developing countries, and conclude with the accomplishments of HINARI.     

Fitting models to data: Interaction versus polynomial? your choice

Polynomials are widely used for fitting models empirically to data. Low-degree polynomials (specifically, degrees 1, 2, and at most 3) have stood the test of time by proving their versatility when it comes to fitting a wide variety of different surface shapes over limited regions of interest. However, when faced with modeling a surface over an experimental region whose boundaries extend beyond some localized neighborhood or limited-sized region of interest, a polynomial of degree 2, or even of degree 3, may not be adequate. For this situation we propose fitting an interaction model which is a reduced form of higher-degree polynomial. Some examples of actual experiments are presented to illustrate the improvement in fit by an interaction model over that of a standard polynomial, even for response surfaces with uncomplicated shapes.     

Wavelets and estimation of long memory in nonstationary models: Does anything beat the exact local whittle estimator?

In this article, we analyze the performance of five estimation methods for the long memory parameter d. The goal of our article is to construct a wavelet estimate for the fractional differencing parameter in nonstationary long memory processes that dominate the well-known estimate of Shimotsu and Phillips (2005) Shimotsu, K., Phillips, P. (2005). Exact local whittle estimation of fractional integration. Annals of statistics 20:87–127. [Google Scholar]. The simulation results show that the wavelet estimation method of Lee (2005) Lee, J. (2005). Estimating memory parameter in the US inflation rate. Economics Letters 87:207–210. [Google Scholar] with several tapering techniques performs better under most cases in nonstationary long memory. The comparison is based on the empirical root mean squared error of each estimate.     

What is Mentoring?

What is mentoring? Is it just a buzz word or is this really valuable? How can mentoring help one to grow and advance personally and professionally? How and where does one even begin? Many of us have these questions. In this article, I will share my perspective and provide some reflections on these questions based on my own personal and professional journey.     

Multivariate generalized linear mixed models with?semi-nonparametric and smooth nonparametric random effects densities

We extend the family of multivariate generalized linear mixed models to include random effects that are generated by smooth densities. We consider two such families of densities, the so-called semi-nonparametric (SNP) and smooth nonparametric (SMNP) densities. Maximum likelihood estimation, under either the SNP or the SMNP densities, is carried out using a Monte Carlo EM algorithm. This algorithm uses rejection sampling and automatically increases the MC sample size as it approaches convergence. In a simulation study we investigate the performance of these two densities in capturing the true underlying shape of the random effects distribution. We also examine the implications of misspecification of the random effects distribution on the estimation of the fixed effects and their standard errors. The impact of the assumed random effects density on the estimation of the random effects themselves is investigated in a simulation study and also in an application to a real data set.     

Why use arbitrary points scores?: ordered categories in models of educational progress

Much statistical modelling of random effects on ordered responses, particularly of grades in educational research, continues to use linear models and to treat the responses through arbitrary scores. Methodological and software developments now facilitate the proper treatment of such situations through more realistic generalized random-effects models. This paper reviews some methodological comparisons of these approaches. It highlights the flexibility offered by the macro facilities of the multilevel random-effects software MLwiN. It considers applications to an analysis of primary school educational progress from reception to England and Wales national curriculum key stage 1 mathematics. By contrasting the results from generalized modelling and scoring approaches it draws some conclusions about the theoretical, methodological and practical options that are available. It also considers that results of generalized random-model estimation may be more intelligible to users of analytical results.     

The mad estimator: when is it non-linear? applications to two-way design models

The mean absolute deviation (MAD) estimator has recently received a great deal of attention as applied to full-rank linear regression models. This paper provides a necessary and sufficient condition for the MAD estimator to be a non-linear estimator, in which case conditions for the variance of the MAD estimator to be larger or smaller than those for OLS are, in general, unknown. The non-linearity of the MAD estimator is examined for several two-way designs; in particular (1) randomized block design (2) two-way nested design (3) two-way classification with interaction and (4) partially balanced incomplete block design     

Yule–Walker type estimators in periodic bilinear models: strong consistency and asymptotic normality

This paper studies the covariance structure and the asymptotic properties of Yule–Walker (YW) type estimators for a bilinear time series model with periodically time-varying coefficients. We give necessary and sufficient conditions ensuring the existence of moments up to eighth order. Expressions of second and third order joint moments, as well as the limiting covariance matrix of the sample moments are given. Strong consistency and asymptotic normality of the YW estimator as well as hypotheses testing via Wald’s procedure are derived. We use a residual bootstrap version to construct bootstrap estimators of the YW estimates. Some simulation results will demonstrate the large sample behavior of the bootstrap procedure.     

What price semiparametric Cox regression?

Lifetime Data Analysis - Cox’s proportional hazards regression model is the standard method for modelling censored life-time data with covariates. In its standard form, this method relies on...