Influence diagnostics in the capital asset pricing model under elliptical distributions

In this paper we consider the Capital Asset Pricing Model under Elliptical (symmetric) Distributions. This class of distributions, which contains the normal distribution, t, contaminated normal and power exponential, among others, offers a more flexible framework for modelling asset prices or returns. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators, the local influence method was implemented. The results are illustrated by using a set of shares from companies who trade in the Chilean Stock Market. Our main conclusion is that symmetric distributions having heavier tails than those of the normal distribution, especially the t distribution with small degrees of freedom, show a better fit and allow the reduction of the influence of atypical returns in the maximum likelihood estimators.     

我国居民家庭生命周期消费储蓄行为动态模拟研究

本文建立了家庭生命周期消费储蓄优化模拟模型,并利用该模型对我国居民家庭生命周期阶段各类家庭的消费储蓄行为进行了模拟研究,比较分析了家庭生育决策、教育决策和收入转移决策变化对家庭消费储蓄行为的影响以及利率变动和信贷约束的影响。模拟结果显示,信贷约束对家庭消费路径有着重要影响,约束了家庭在新生阶段和中间阶段的消费水平;教育费用的提高和教育投资实现的不确定性风险提高了家庭的教育储蓄水平;生育数的降低、教育投资机会的短缺和代际关系的松散化趋势提高了家庭的养老储蓄水平。利率的提高使生命周期前期的家庭消费下降,使生命周期后期的家庭消费提高。提高利率政策的消费传导效应,可以通过提高受教育程度和增加消费信贷来实现。     

A cell means analysis of mixed linear models

The purpose of this paper is to describe a simple procedure for the estima-tion of parameters in the unbalanced mixed linear model. There are implications for hypothesis testing and interval estimation, A feature of these estimators is that they are expressed in terms of simple formulas. This has obvious advantages for computations and small sample analysis. In addition, the formulas suggest useful diagnostic procedures for assessing the quality of the data as well as possible defects in the model assumptions. The concepts are illustrated with several examples. Evidence is presented to indicate that, in cases of modest imbalance, these estimators are highly efficient and dominate AOV estimates over most of the parameter space. In cases of more extreme imbalance, the results are qualitatively the same but the estimators are less efficient than the AOV estimators for small values of the parameters. The extension of this method to factorial models with missing cells is not complete.     

Influence Diagnostics of Semiparametric Nonlinear Reproductive Dispersion Models

This article proposes a semiparametric nonlinear reproductive dispersion model (SNRDM) which is an extension of nonlinear reproductive dispersion model and semiparametric regression model. Maximum penalized likelihood estimators (MPLEs) of unknown parameters and nonparametric functions in SNRDMs are presented. Some novel diagnostic statistics such as Cook distance and difference deviance for parametric and nonparametric parts are developed to identify influence observations in SNRDMs on the basis of case-deletion method, and some formulae readily computed with the MPLEs algorithm for diagnostic measures are given. The equivalency of case-deletion models and mean-shift outlier models in SNRDM is investigated. A simulation study and a real example are used to illustrate the proposed diagnostic measures.     

Detecting influential observations in Liu and modified Liu estimators

In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results.     

A multiple-case deletion approach for detecting influential points in high-dimensional regression

ABSTRACT

In high-dimensional regression, the presence of influential observations may lead to inaccurate analysis results so that it is a prime and important issue to detect these unusual points before statistical regression analysis. Most of the traditional approaches are, however, based on single-case diagnostics, and they may fail due to the presence of multiple influential observations that suffer from masking effects. In this paper, an adaptive multiple-case deletion approach is proposed for detecting multiple influential observations in the presence of masking effects in high-dimensional regression. The procedure contains two stages. Firstly, we propose a multiple-case deletion technique, and obtain an approximate clean subset of the data that is presumably free of influential observations. To enhance efficiency, in the second stage, we refine the detection rule. Monte Carlo simulation studies and a real-life data analysis investigate the effective performance of the proposed procedure.     

Bayesian Binomial Regression: Predicting Survival at a Trauma Center

Standard methods for analyzing binomial regression data rely on asymptotic inferences. Bayesian methods can be performed using simple computations, and they apply for any sample size. We provide a relatively complete discussion of Bayesian inferences for binomial regression with emphasis on inferences for the probability of “success.” Furthermore, we illustrate diagnostic tools, perform model selection among nonnested models, and examine the sensitivity of the Bayesian methods.     

Descriptive tests for the identification of outliers in the errors in variables linear model

In this paper the most commonly used diagnostic criteria for the identification of outliers or leverage points in the ordinary regression model are reviewed. Their use in the context of the errors-in-variables (e.v.) linear model is discussed and evidence is given that under the e.v. model assumptions the distinction between outliers and leverage points no longer exists.     

基于空间分割的神经网络方向估计

在空间分割的基础上，提出了采用四个独立的二次规划神经网络ｎｅｕｒａｌｎｅｔｗｏｒｋｓ（ＮＮ）实现方向估计的方法。通过将空间角度分割成许多细小的等分，从这些不同的角度估计出沿该角度入射信号的幅度，从而判断出目标信号的个数和方向，利用该方法设计的网络具有高速、实时的特点，计算机模拟结果表明该方法很有效。     

论公民道德建设的基本原则

公民道德建设应该怎样进行,这既是理论也是实践的焦点。中国在建设社会主义法制国家的进程中,公民道德观念的建设应当与法制国家的观念建设同步。本文从传统伦理的观点出发,分析了中国当代社会的现状,坚持历史与现实的统一、综合与分析的思维方法,提出了道德建设的与时俱进、文以载道和统一性三项基本原则。