NON-PARAMETRIC ESTIMATION OF DIRECTION IN SINGLE-INDEX MODELS WITH CATEGORICAL PREDICTORS

This paper proposes a general dimension‐reduction method targeting the partial central subspace recently introduced by Chiaromonte, Cook & Li. The dependence need not be confined to particular conditional moments, nor are restrictions placed on the predictors that are necessary for methods like partial sliced inverse regression. The paper focuses on a partially linear single‐index model. However, the underlying idea is applicable more generally. Illustrative examples are presented.     

Efficient estimation for time series following generalized linear models

In this paper, we consider James–Stein shrinkage and pretest estimation methods for time series following generalized linear models when it is conjectured that some of the regression parameters may be restricted to a subspace. Efficient estimation strategies are developed when there are many covariates in the model and some of them are not statistically significant. Statistical properties of the pretest and shrinkage estimation methods including asymptotic distributional bias and risk are developed. We investigate the relative performances of shrinkage and pretest estimators with respect to the unrestricted maximum partial likelihood estimator (MPLE). We show that the shrinkage estimators have a lower relative mean squared error as compared to the unrestricted MPLE when the number of significant covariates exceeds two. Monte Carlo simulation experiments were conducted for different combinations of inactive covariates and the performance of each estimator was evaluated in terms of its mean squared error. The practical benefits of the proposed methods are illustrated using two real data sets.     

几种语言的WH部分移位特征推导

德语、波兰语等语言wh成分部分移位问句发生wh特征移位,其带有i[Q]和u[wh]*特征的标句中心词C与带有i[wh]和u[Q]特征的wh成分进行多重一致关系特征核查使后者处于低于句首辖域位置的Spec-CP位置.匈牙利语、阿尔巴尼亚语等语言wh成分部分移位问句进行焦点特征移位,其带有i[Q]和u[Fbc]*特征的焦点中心词F与带有i[Foc]和u[Q]特征的wh成分进行Foc特征一致关系核查并使后者前移置于CP之后的Spec-FP位置.     

Completeness and optimality of marginal likelihood estimating equations

A counter-example shows that the proof of optimality of the marginal likelihood estimating function for parameter of interest, under the conditions assumed in Lloyd (1987), contains a gap and is, thus, invalid. The same comment applies to the generalized version of Lloyd’s Theorem given by Bhapkar and Srinivasan (1993). In the light of known results concerning Fisher information for parameter of interest and partial sufficiency of a suitable statistic, the counter-example reveals a similar gap in the proof of corollary 3.2 of Bhapkar (1991).     

Elasticity of Factor Substitution in Police Agencies Evidence From Florida

A multiproduct translog cost function is estimated and used to calculate elasticities of factor substitution in the production of police services. The data come from Florida's municipal police departments and are for 1982–1983. It is found that substitution elasticities between any pair of inputs in police production are significantly different from unity. This result is inconsistent with the Cobb–Douglas production function—a functional form that has been extensively employed in empirical studies of police production.     

信息不完全应急物资转运系统的随机Petri网建模

对信息不完全条件下的应急物资转运系统建立了一个随机Petri网模型。根据这一模型，找到了计算各个状态中驻留时间、卡车的平均利用率、物资分发点等待物资的平均等待概率、物资分发点等待物资的平均等待时间的方法。使用这个模型对某企业在汶川特大地震应急物资保障中的物资转运系统的性能进行了计算，性能计算的主要结果与实际统计数据吻合。     

Analysis of multiple sets of incomplete rankings

The L statistic is extended to allow incomplete or partial rankings within groups. As a special case of these extensions, tests for ofered alternatives analogous to the page test are provided for other than complete rankings. A two group illustration is provided by an alternative analysis of the Bradley and Terry taste-testing experiment on pork-roasts.     

SNP selection for predicting a quantitative trait

Molecular markers combined with powerful statistical tools have made it possible to detect and analyze multiple loci on the genome that are responsible for the phenotypic variation in quantitative traits. The objectives of the study presented in this paper are to identify a subset of single nucleotide polymorphism (SNP) markers that are associated with a particular trait and to construct a model that can best predict the value of the trait given the genotypic information of the SNPs using a three-step strategy. In the first step, a genome-wide association test is performed to screen SNPs that are associated with the quantitative trait of interest. SNPs with p-values of less than 5% are then analyzed in the second step. In the second step, a large number of randomly selected models, each consisting of a fixed number of randomly selected SNPs, are analyzed using the least angle regression method. This step will further remove redundant SNPs due to the complicated association among SNPs. A subset of SNPs that are shown to have a significant effect on the response trait more often than by chance are considered for the third step. In the third step, two alternative methods are considered: the least angle shrinkage and selection operation and sparse partial least squares regression. For both methods, the predictive ability of the fitted model is evaluated by an independent test set. The performance of the proposed method is illustrated by the analysis of a real data set on Canadian Holstein cattle.     

An algorithm for the evaluation of the incomplete gamma function and the first two partial derivatives with respect to the parameter

A simple to use, straight coded, Fortran 4 algorithm, is presented. This algorithm has the ability to:

1) evaluate the Incomplete Gamma function, Y(r,λx), for parameter values in the range 0 < r < 20.0 and upper limit of integration values in the range 0 75.0;

2) evaluate both the first and second partial derivatives of y with respect to the parameter

3) evaluate both the Euler Di and Trigamma functions, ψ(r) and if ψ′(r)for 0<rf.20.0.

In all cases the accuracy is nine or more significant figures. The user has several choices of data output format     

Catanova for multidimensional contingency tables: Nominal-scale response

This paper extends an analysis of variance for categorical data (CATANOVA) procedure to multidimensional contingency tables involving several factors and a response variable measured on a nominal scale. Using an appropriate measure of total variation for multinomial data, partial and multiple association measures are developed as R² quantities which parallel the analogous statistics in multiple linear regression for quantitative data. In addition, test statistics are derived in terms of these R² criteria. Finally, this CATANOVA approach is illustrated within the context of 2 three-way contingency table from a multicenter clinicaltrial.