Development of r,T hybrid sampling plans for exponential lifetime distributions

SUMMARY In the original hybrid reliability acceptance sampling plan RASP developed by Epstein for the exponential lifetime distribution, the sample size n and the number of failures r to be observed are determined given the truncation time T . However, these plans cannot be used when test items are expensive and or limited in number. Therefore, one wishes to have a plan in which n is given. In this paper, we develop hybrid RASPs in which r and T are determined, given n. These plans are tabulated for various combinations of parameter values, so that one may easily determine an appropriate plan.     

To market, to market: the changing face of grocery retailing

Over the past few years, grocery retailing has undergone a tremendous transformation which was driven mainly by changing consumer characteristics. In order to accommodate these changes, the industry has responded in three ways. The most important of these is the introduction of new technology, particularly efficient consumer response (ECR). The second change has to do with innovations in product and/or service offerings. The final transformation is the organisational restructuring and the increasing number of mergers and acquisitions in the industry at both the manufacturer and retailer levels. The implications of these transformations on the marketing mix, productivity and manufacturer-retailer relationships are discussed.     

Oracle properties of SCAD-penalized support vector machine

In many scientific investigations, a large number of input variables are given at the early stage of modeling and identifying the variables predictive of the response is often a main purpose of such investigations. Recently, the support vector machine has become an important tool in classification problems of many fields. Several variants of the support vector machine adopting different penalties in its objective function have been proposed. This paper deals with the Fisher consistency and the oracle property of support vector machines in the setting where the dimension of inputs is fixed. First, we study the Fisher consistency of the support vector machine over the class of affine functions. It is shown that the function class for decision functions is crucial for the Fisher consistency. Second, we study the oracle property of the penalized support vector machines with the smoothly clipped absolute deviation penalty. Once we have addressed the Fisher consistency of the support vector machine over the class of affine functions, the oracle property appears to be meaningful in the context of classification. A simulation study is provided in order to show small sample properties of the penalized support vector machines with the smoothly clipped absolute deviation penalty.     

On Fisher’s dispersion test for integer-valued autoregressive Poisson models with applications

The integer-valued autoregressive (INAR) model has been widely used in diverse fields. Since the task of identifying the underlying distribution of time-series models is a crucial step for further inferences, we consider the goodness-of-fit test for the Poisson assumption on first-order INAR models. For a test, we employ Fisher’s dispersion test due to its simplicity and then derive its null limiting distribution. As an illustration, a simulation study and real data analysis are conducted for the counts of coal mining disasters, the monthly crime data set from New South Wales, and the annual numbers of worldwide earthquakes.     

The mixed model for survey regression estimation

Estimation of the population mean under the regression model with random components is considered. Conditions under which the random components regression estimator is design consistent are given. It is shown that consistency holds when incorrect values are used for the variance components. The regression estimator constructed with model parameters that differ considerably from the true parameters performed well in a Monte Carlo study. Variance estimators for the regression predictor are suggested. A variance estimator appropriate for estimators constructed with a biased estimator for the between-group variance component performed well in the Monte Carlo study.     

Bayesian analysis for incomplete multi-way contingency tables with nonignorable nonresponse

We propose Bayesian methods with five types of priors to estimate cell probabilities in an incomplete multi-way contingency table under nonignorable nonresponse. In this situation, the maximum likelihood (ML) estimates often fall in the boundary solution, causing the ML estimates to become unstable. To deal with such a multi-way table, we present an EM algorithm which generalizes the previous algorithm used for incomplete one-way tables. Three of the five types of priors were previously introduced while the other two are newly proposed to reflect different response patterns between respondents and nonrespondents. Data analysis and simulation studies show that Bayesian estimates based on the old three priors can be worse than the ML regardless of occurrence of boundary solution, contrary to previous studies. The Bayesian estimates from the two new priors are most preferable when a boundary solution occurs. We provide an illustrating example using data for a study of the relationship between a mother's smoking and her newborn's weight.     

A Bayesian approach for improved pavement performance prediction

We present a method for predicting future pavement distresses such as longitudinal cracking. These predicted distress values are used to plan road repairs. Large inherent variability in measured cracking and an extremely small number of observations are the nature of the pavement cracking data, which calls for a parametric Bayesian approach. We model theoretical pavement distress with a sigmoidal equation with coefficients based on prior engineering knowledge. We show that a Bayesian formulation akin to Kalman filtering gives sensible predictions and provides defendable uncertainty statements for predictions. The method is demonstrated on data collected by the Texas Transportation Institute at several sites in Texas. The predictions behave in a reasonable and statistically valid manner.     

Relative error prediction via kernel regression smoothers

In this article, we introduce and study local constant and local linear nonparametric regression estimators when it is appropriate to assess performance in terms of mean squared relative error of prediction. We give asymptotic results for both boundary and non-boundary cases. These are special cases of more general asymptotic results that we provide concerning the estimation of the ratio of conditional expectations of two functions of the response variable. We also provide a good bandwidth selection method for the estimators. Examples of application, limited simulation results and discussion of related problems and approaches are also given.     

Asian and Pacific Region Authorship Characteristics in Leading Library and Information Science Journals

Authorship characteristics from the Asian and Pacific region in the top twenty journals in library and information science are studied. Data was collected searching the Institute for Scientific Information (ISI) Web of Science databases. Major findings of this study are: there are a total of 1,317 articles for the period 1967 to 2005; the most productive countries are, in rank order, Australia, China, South Korea, Taiwan, Singapore, Japan, New Zealand, Malaysia, Thailand and Philippines; and 77.6 percent of authors in the top twenty library and information science journals contributed a single article. Among the library science journals about 50 percent were written by multiple authors, while 73.1 percent of articles in the information science journals were written collaboratively. The most productive individual authors in the region are reported. The strongest collaboration within the region took place between Australia and China; China and Singapore; Australia and New Zealand.