This paper discusses the econometric methodology of general-to-specific modeling, in which the modeler simplifies an initially general model that adequately characterizes the empirical evidence within his or her theoretical framework. Central aspects of this approach include the theory of reduction, dynamic specification, model selection procedures, model selection criteria, model comparison, e...

This paper discusses the econometric methodology of general-to-specific modeling, in which the modeler simplifies an initially general model that adequately characterizes the empirical evidence within his or her theoretical framework. Central aspects of this approach include the theory of reduction, dynamic specification, model selection procedures, model selection criteria, model comparison, e...

We present two fully sequential indifference-zone procedures to select the best system from a number of competing simulated systems when best is defined by maximum or minimum expected performance. These two procedures have parabola shaped continuation regions rather than triangular continuation regions employed in several papers. The procedures we present accommodate unequal and unknown varianc...

SUMMARY Suppose one has k populations and wishes eventually to choose the "best" one. It is often desirable to use a multi-stage experiment, where at each stage, at least t (~l) populations are selected for further study. This paper is concerned with selecting at least t(~2) populations, presenting generalizations of two methods in existence for t = 1. One method has as its goal the selection o...

Two-stage indifference-zone selection procedures have been widely studied and applied. It is known that most indifference-zone selection procedures also guarantee multiple comparisons with the best confidence intervals with half-width corresponding to the indifference amount. We provide the statistical analysis of multiple comparisons with a control confidence interval that bounds the differenc...

In this paper the guidelines for applying heteroscedastic linear models for analysing industrial process data is presented. Heteroscedastic linear models are considered as a good model family for the joint modelling of dispersion and mean. The model selection of heteroscedastic linear model is discussed considering the special features of industrial data. A procedure for dispersion model select...

S sampling problems arise in stochastic simulation and many other applications. Sampling is used to infer the unknown performance of several alternatives before one alternative is selected as best. This paper presents new economically motivated fully sequential sampling procedures to solve such problems, called economics of selection procedures. The optimal procedure is derived for comparing a ...

Many variable selection methods for linear regression depend critically on tuning parameters that control the performance of the method, e.g., “entry” and“stay” significance levels in forward and backward selection. However, most methods do not adapt the tuning parameters to particular data sets. We propose a general strategy for adapting variable selection tuning parameters that effectively es...

We consider the problem of variable selection in general nonlinear mixed-e ets models, including mixed-e ects hidden Markov models. These models are used extensively in the study of repeated measurements and longitudinal analysis. We propose a Bayesian Information Criterion (BIC) that is appropriate for nonstandard situations where both the number of subjects N and the number of measurements pe...