Statistical Inference for Single‐index Panel Data Models

We study estimation and hypothesis testing in single‐index panel data models with individual effects. Through regressing the individual effects on the covariates linearly, we convert the estimation problem in single‐index panel data models to that in partially linear single‐index models. The conversion is valid regardless of the individual effects being random or fixed. We propose an estimating equation approach, which has a desirable double robustness property. We show that our method is applicable in single‐index panel data models with heterogeneous link functions. We further design a chi‐squared test to evaluate whether the individual effects are random or fixed. We conduct simulations to demonstrate the finite sample performance of the method and conduct a data analysis to illustrate its usefulness.     

Estimation and Comparison of Signed Symmetric Covariation Coefficient and Generalized Association Parameter for Alpha-stable Dependence Modeling

In this paper, we study the estimators of two measures of dependence: the signed symmetric covariation coefficient (scov) proposed by Garel and Kodia and the generalized association parameter (g.a.p.) put forward by Paulauskas. In the sub-Gaussian case, the scov and the g.a.p. coincide. The estimator of the scov proposed here is based on fractional lower-order moments. The estimator of the g.a.p. is based on estimation of a stable spectral measure. We investigate the relative performance of these estimators by comparing results from simulations.     

A table of some percentage points of the distribution of a difference between independent chi-square variables

The distribution of Bell-Doksum measure of correlation is that of a difference between independent chi-square variables with equal weights. A table of percentage points computed here for the distribution may be used to test a hypothesis of no correlation between two variables. The distribution of a diffference between independent chi-square variables is also useful in studying variance component estimators and some general results corresponding to the distribution are given.     

Skewed zero-bound distributions and process capability indices for upper specifications

A common practical situation in process capability analysis, which is not well developed theoretically, is when the quality characteristic of interest has a skewed distribution with a long tail towards relatively large values and an upper specification limit only exists. In such situations, it is not uncommon that the smallest possible value of the characteristic is 0 and this is also the best value to obtain. Hence a target value 0 is assumed to exist. We investigate a new class of process capability indices for this situation. Two estimators of the proposed index are studied and the asymptotic distributions of these estimators are derived. Furthermore, we suggest a decision procedure useful when drawing conclusions about the capability at a given significance level, based on the estimated indices and their asymptotic distributions. A simulation study is also performed, assuming that the quality characteristic is Weibull-distributed, to investigate the true significance level when the sample size is finite.     

A note on information theoretic measures

An axiomatic characterization of an information theoretic quantity is given using only two axioms. In particular, this quantity contain PEARSON'S chi-square statistic and some other useful measures     

An MM Algorithm for Constrained Estimation in a Road Safety Measure Modeling

We model the effect of a road safety measure on a set of target sites with a control area for each site, and we suppose that the accident data recorded at each site are classified in different mutually exclusive types. We adopt the before–after technique and we assume that at any one target site the total number of accidents recorded is multinomially distibuted between the periods and types of accidents. In this article, we propose a minorization–majorization (MM) algorithm for obtaining the constrained maximum likelihood estimates of the parameter vector. We compare it with a gradient projection–expectation maximization (GP-EM) algorithm, based on gradient projections. The performance of the algorithms is examined through a simulation study of road safety data.     

Standards of Probability Sampling for Legal Evidence

Brief recognition is given to bicentennials of the death of James Stirling (1692–1770) and birth of Ferdinand Rudolph Hassler (1770–1843); to sesquicentennials of the publication of the 3rd definitive edition of Laplace's Théorie analytique des probabilités, and of the births of William Chauvenet (1820–1870) and Isaac Todhunter (1820–1884); to centennials of the births of Louis Bachelier (1870–1946) and Jean Perrin (1870–1942); and to the semicentennial of the initial publication (1920) of Metron, an International Review of Statistics.     

A class of closeness criteria

A generalized class of closeness criteria for the pairwise comparison of esti¬mators is defined. This class contains an infinite number of members including Pitman's measure of closeness and at least one transitive criterion. Several specific members of the class are examined, and their relationships to Rao concentration and stochastic domination are shown. Graphical and analyti¬cal characterizations are shown for these members of the class. Examples are given which illustrate the behavior of some of these criteria.     

Estimating capability index cpk for processes with asymmetric tolerances

Pearn and Chen (1996) considered the process capability index C_pk, and investigated the statistical properties of its natural estimator under various process conditions. Their investigation, however, was restricted to processes with symmetric tolerances. Recently, Pearn and Chen (1998) considered a generalization of C_pk, referred to as C^? _pk, to cover processes with asymmetric tolerances. They investigated the statistical properties of the natural estimator of C^? _pk, and obtained the exact formulae for the expected value and variance. In this paper, we consider a new estimator of C^? _pk, assuming the knowledge on P(LI > T) = p is available, where 0 > p > 1, which can be obtained from historical information of a stable process. We obtain the exact distribution of the new estimator assuming the process characteristic follows the normal distribution. We show that the new estimator is consistent, asymptotically unbiased, which converges to a mixture of two normal distributions. We also show that by adding suitable correction factors to the new estimator, we may obtain the UMVUE and the MLE of the generalization C^? _pk.     

Masstige model and measure for brand management

The purpose of this article is to reconceptualize the term “masstige” (Mass Prestige) marketing, develop a masstige model for brand management, and extend the use of the Masstige Mean Scale (MMS). The study was conducted based on the data from 600 individuals living in the United States, France, and India using a structured questionnaire consisting of different factors/sources of brand equity, such as mass prestige, brand knowledge, and perceived quality. On the basis of the findings, we establish that the greater the brand's Masstige Mean Index (MMI) value (“MMIV”), the higher the potential customers' top-of-mind brand awareness. Low MMIVs imply that firms have a long way to go to build their brands. We argue that MMI may allow firms to measure brand equity in different regions, within a country or in foreign countries, to derive insights into the popularity of their brands. We posit three theoretical propositions and develop two theoretical models (i) a hexagon model and (ii) a three-stage model for masstige marketing to define, reconceptualize, and explain the phenomenon.