General definition and decomposition of projectors and some applications to statistical problems

A general definition of a set of projectors for decomposing a vector as the sum of vectors belonging to disjoint subspaces not necessarily spanning the whole space is given. Such projectors are defined only over the union of the disjoint subspaces. But their extension to the whole space is of some interest in statistical problems. Explicit expressions are obtained for projectors and their extensions in terms of matrices spanning the subspaces and g-inverses. Decomposition of a projector as the sum of projectors on subspaces is obtained and applied to problems arising in correlation analysis, analysis of variance and estimation of parameters in the Gauss-Markoff model.     

Characterizations of generalized mixtures of geometric and exponential distributions based on upper record values

Let X _{U (1)} < X _{U (2)} < … < X _{U ( n )} < … be the sequence of the upper record values from a population with common distribution function F. In this paper, we first give a theorem to characterize the generalized mixtures of geometric distribution by the relation between E[(X _{U ( n +1)}–X _{U ( n )})²|X _{U ( n )} = x] and the function of the failure rate of the distribution, for any positive integer n. Secondly, we also use the same relation to characterize the generalized mixtures of exponential distribution. The characterizing relations were motivated by the work of Balakrishnan and Balasubramanian (1995). Received: March 31, 1999; revised version: November 22, 1999     

A simple method for estimating a regression model for κ between a pair of raters

Agreement studies commonly occur in medical research, for example, in the review of X-rays by radiologists, blood tests by a panel of pathologists and the evaluation of psychopathology by a panel of raters. In these studies, often two observers rate the same subject for some characteristic with a discrete number of levels. The κ-coefficient is a popular measure of agreement between the two raters. The κ-coefficient may depend on covariates, i.e. characteristics of the raters and/or the subjects being rated. Our research was motivated by two agreement problems. The first is a study of agreement between a pastor and a co-ordinator of Christian education on whether they feel that the congregation puts enough emphasis on encouraging members to work for social justice (yes versus no). We wish to model the κ-coefficient as a function of covariates such as political orientation (liberal versus conservative) of the pastor and co-ordinator. The second example is a spousal education study, in which we wish to model the κ-coefficient as a function of covariates such as the highest degree of the father of the wife and the father of the husband. We propose a simple method to estimate the regression model for the κ-coefficient, which consists of two logistic (or multinomial logistic) regressions and one linear regression for binary data. The estimates can be easily obtained in any generalized linear model software program.     

Non-conjugate prior distribution assessment for multivariate normal sampling

Elicitation methods are proposed for quantifying expert opinion about a multivariate normal sampling model. The natural conjugate prior family imposes a relationship between the mean vector and the covariance matrix that can portray an expert's opinion poorly. Instead we assume that opinions about the mean and the covariance are independent and suggest innovative forms of question which enable the expert to quantify separately his or her opinion about each of these parameters. Prior opinion about the mean vector is modelled by a multivariate normal distribution and about the covariance matrix by both an inverse Wishart distribution and a generalized inverse-Wishart (GIW) distribution. To construct the latter, results are developed that give insight into the GIW parameters and their interrelationships. Certain of the elicitation methods exploit unconditional assessments as fully as possible, since these can reflect an expert's beliefs more accurately than conditional assessments. Methods are illustrated through an example.     

Generalized least squares with misspecified serial correlation structures

Summary. The regression literature contains hundreds of studies on serially correlated disturbances. Most of these studies assume that the structure of the error covariance matrix Ω is known or can be estimated consistently from data. Surprisingly, few studies investigate the properties of estimated generalized least squares (GLS) procedures when the structure of Ω is incorrectly identified and the parameters are inefficiently estimated. We compare the finite sample efficiencies of ordinary least squares (OLS), GLS and incorrect GLS (IGLS) estimators. We also prove new theorems establishing theoretical efficiency bounds for IGLS relative to GLS and OLS. Results from an exhaustive simulation study are used to evaluate the finite sample performance and to demonstrate the robustness of IGLS estimates vis-à-vis OLS and GLS estimates constructed for models with known and estimated (but correctly identified) Ω. Some of our conclusions for finite samples differ from established asymptotic results.     

Generalized Empirical Likelihood Inference in Generalized Linear Models for Longitudinal Data

In this article, the generalized linear model for longitudinal data is studied. A generalized empirical likelihood method is proposed by combining generalized estimating equations and quadratic inference functions based on the working correlation matrix. It is proved that the proposed generalized empirical likelihood ratios are asymptotically chi-squared under some suitable conditions, and hence it can be used to construct the confidence regions of the parameters. In addition, the maximum empirical likelihood estimates of parameters are obtained, and their asymptotic normalities are proved. Some simulations are undertaken to compare the generalized empirical likelihood and normal approximation-based method in terms of coverage accuracies and average areas/lengths of confidence regions/intervals. An example of a real data is used for illustrating our methods.     

Partisan strength,political trust and generalized trust in the United States: An analysis of the General Social Survey, 1972–2014

The literature on political parties suggests that strong partisan identities are associated with citizens’ effective interaction with the political system, and with higher levels of political trust. Traditionally, party identity therefore is seen as a mechanism that allows for political integration. Simultaneously, however, political parties have gained recent attention for their role in promoting societal polarization by reinforcing competing and even antagonistic group identities. This article uses General Social Survey data from 1972 – 2014 to investigate the relationship between partisan strength and both political and generalized trust. The findings show that increases in partisan strength are positively related to political trust, but negatively related to generalized trust. This suggests that while partisan strength is indeed an important linkage mechanism for the political system, it is also associated with a tendency toward social polarization, and this corrosive effect thus far has not gained sufficient attention in literature on party identity.     

On von Mises type conditions for p-max stable laws, rates of convergence and generalized log Pareto distributions

In this article we obtain an alternative formulation of the von Mises type conditions for p-max stable laws in terms of generalized log Pareto distributions (glogPds). Relationship between the rate of convergence of extremes and the remainder terms in the von Mises type conditions is investigated. It is shown that the rate of convergence in the von Mises type conditions for p-max stable laws determines the distance of the underlying distribution function from a glogPd.     

Monotone support vector quantile regression

Quantile regression (QR) models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper we propose support vector quantile regression (SVQR) with monotonicity restriction, which is easily obtained via the dual formulation of the optimization problem. We also provide the generalized approximate cross validation method for choosing the hyperparameters which affect the performance of the proposed SVQR. The experimental results for the synthetic and real data sets confirm the successful performance of the proposed model.     

Nonparametric hypothesis testing in clustered survival model

A nonparametric test for the presence of clustering in survival data is proposed. Assuming a model that incorporates the clustering effect into the Cox Proportional Hazards model, simulation studies indicate that the procedure is correctly sized and powerful in a reasonably wide range of scenarios. The test for the presence of clustering over time is also robust to model misspecification. With large number of clusters, the test is powerful even if the data is highly heterogeneous.