Large Deviation Principles for Sequences of Maxima and Minima

In this article, we consider sequences of i.i.d. random variables and, under suitable conditions on the (common) distribution function, we prove large deviation principles for sequences of maxima, minima and pairs formed by maxima and minima. The i.i.d. random variables can be either unbounded or bounded; in the first case maxima and minima have to be suitably normalized.     

Estimation and Prediction for Exponentiated Family of Distributions Based on Records

In this article, a family of distributions, namely the exponentiated family of distributions, is defined and for the unknown parameters, different point estimates are derived based on record statistics. Prediction for future record values is presented from a Bayesian view point. Two numerical examples and a Monte Carlo simulation study are presented to illustrate the results.     

Goals for Statistical Uses of Administrative Records: The Next 10 Years

United States statistical agencies use data from administrative record systems to develop program statistics, to establish statistical data bases, and to enhance and evaluate census and survey data. Such uses of administrative records are likely to increase as efforts to control costs and respondent burden of statistical programs continue. This review article proposes six goals for enhanced statistical uses of administrative records in the next 10 years and describes elements of an activist strategy to achieve them. The discussants, representing three agencies that make important statistical uses of administrative records, give their reactions to the proposed goals and strategy.     

A Review on Characterizations of Discrete Distributions Based on Records and kth Records

This is a survey of characterizations of discrete distributions via properties of record values. Characterization results based on records and weak records are presented. Then the concepts of kth records, strong kth records, and weak kth records are recalled. Finally, characterizations of the geometric parent involving these three types of kth records are discussed.     

Statistical Inference for a New Class of Multivariate Pareto Distributions

Various solutions to the parameter estimation problem of a recently introduced multivariate Pareto distribution are developed and exemplified numerically. Namely, a density of the aforementioned multivariate Pareto distribution with respect to a dominating measure, rather than the corresponding Lebesgue measure, is specified and then employed to investigate the maximum likelihood estimation (MLE) approach. Also, in an attempt to fully enjoy the common shock origins of the multivariate model of interest, an adapted variant of the expectation-maximization (EM) algorithm is formulated and studied. The method of moments is discussed as a convenient way to obtain starting values for the numerical optimization procedures associated with the MLE and EM methods.     

A comparison of micro versus macro point estimators for markov-process models

We compare, by means of factorially designed Monte Carlo simulation experiments, the performance of (macro-data) restricted least-squares point estimators with that of (micro-data) maximum likelihood estimators for Markov-process models. We find, by various measures of estimator accuracy, that micro data are approximately ten times more valuable than macro data. We also find evidence that a small state space, a long time series, and a large number of entities observed enhance performance.     

Robust M-estimators of scale: Minimax bias versus maximal variance

In the location-scale estimation problem, we study robustness properties of M-estimators of the scale parameter under unknown ?-contamination of a fixed symmetric unimodal error distribution F₀. Within a general class of M-estimators, the estimator with minimax asymptotic bias is shown to lie within the subclass of α-interquantile ranges of the empirical distribution symmetrized about the sample median. Our main result is that as ? → 0, the limiting minimax asymptotic bias estimator is sometimes (e.g., when F_o is Cauchy), but not always, the median absolute deviation about the median. It is also shown that contamination in the neighbourhood of a discontinuity of the influence function of a minimax bias estimator can sometimes inflate the asymptotic variance beyond that achieved by placing all the ?-contamination at infinity. This effect is quantified by a new notion of asymptotic efficiency that takes into account the effect of infinitesimal contamination of the parametric model for the error distribution.     

Control Variates for Monte Carlo Analysis of Nonlinear Statistical Models,I: Overview

Parameter values of nonlinear statistical models are typically estimated from data using iterative numerical procedures. The resulting joint sampling distribution of the parameter estimators is often intractable, resulting in the use of approximators or Monte Carlo simulation to determine properties of the sampling distribution.

This paper develops methods, using linear and higher-order approximators as control variates that reduce the variance of the Monte Carlo estimator by orders of magnitude. Estimation of means, higher-order raw moments, variances, covariances, and percentiles is considered.     

Comment: Uses of Administrative Records: A Social Security Point of View

Recent evidence indicates that using multiple forward rates sharply predicts future excess returns on U.S. Treasury Bonds, with the R²'s being around 30%. The projection coefficients in these regressions exhibit a distinct pattern that relates to the maturity of the forward rate. These dimensions of the data, in conjunction with the transition dynamics of bond yields, offer a serious challenge to term structure models. In this article we show that a regime-shifting term structure model can empirically account for these challenging data features. Alternative models, such as affine specification, fail to account for these important features. We find that regimes in the model are intimately related to bond risk premia and real business cycles.     

Statistical Analysis for the Inverse Trinomial Distribution

This article considers parameter estimation, goodness of fit, likelihood ratio and score tests, and model selection by Akaike information criterion for the inverse trinomial (IT) distribution, a classical one-dimensional random walk distribution. The IT distribution has a cubic variance function of the mean and is a generalization of the negative binomial distribution. Basic distributional properties and expressions for the probability mass function, recurrence formula, moments, and score functions are also presented.     

宏观经济因素对房地产价格的长短期影响

本文将购房者、房产商与中央银行纳入统一的模型当中,对房价的影响因素进行理论建模,然后拓展了传统的向量自回归模型长短期分解技术,研究了利率、通货膨胀率、汇率、土地价格以及经济增长率对房价的长期和短期影响。研究表明,利率上升对房价具有长期的负向效应,短期内会加剧房价的波动;通胀预期在长期内促使资金流入房地产行业,对房价会产生推动作用,短期内,通胀促使房地产企业加快投资速度,房屋供给增加预期引起房价下降的冲击;人民币升值在长短期内均与房价上涨存在正向作用;在当前的经济和房地产发展阶段,经济增速提高时,对房地产业的投资和投机具有一定的抑制作用;土地价格长期来看推动房价上涨,短期内对房价影响的传导较慢。     

中国西部地区林业生产技术效率的测算和动态演进分析

运用三阶段DEA模型对中国西部地区11个省区2003—2011年的林业生产技术效率进行测算,并利用核密度估计方法对林业生产技术效率的变动趋势进行分析。研究表明:西部地区林业生产技术效率较低,平均值只有0.664;9年间林业生产纯技术效率始终高于林业生产规模效率,较低的规模效率是导致西部地区林业生产技术效率较低的主要原因;2003—2011年期间,西部地区各省区的林业生产技术效率呈现两极分化的趋势。实现西部地区林业生产的规模化发展是提高林业生产技术效率和缩短地区之间效率差距的关键所在。     

中国城镇化的地域非均衡及其动态演进——来自基尼系数及核密度估计的经验证据

基于中国1995-2013年省域数据,采用基尼系数及其分解、核密度估计方法,从人口和土地城镇化入手,系统分析了中国城镇化的地域非均衡及其动态演化规律。结果发现,1.中国人口和土地城镇化分布均呈现出由东往西逐渐降低的规律,城镇化非均衡主要体现在土地城镇化,而人口城镇化则未出现明显分异。2.全国尺度人口城镇化基尼系数随时间不断下降,城镇化非均衡逐渐减小;土地城镇化基尼系数则呈倒"U"型,城镇化非均衡先增后减。3.东中西三大区域内人口城镇化基尼系数均呈直线下降,区域间非均衡东部最大,西部次之,中部最小;土地城镇化非均衡则是东部大于中部和西部,但近年来西部已超过东部。4.人口城镇化非均衡在1995-2001年间主要来自地区间重叠,而后2002-2013年主要由地区间差异驱动;土地城镇化非均衡则主要来源于地区间差异。5.核密度估计显示人口城镇化增速较快,波动较小,而土地城镇化则极化趋势明显,波动较大。新型城镇化的协调推进宜从人口和土地城镇化两方面着手,特别要注意土地城镇化的失衡发展问题。     

Line transects of two-dimensional random fields: Estimation and design

In line-transect sampling data are collected continuously along the path travelled. These data are then used to estimate some characteristic of an entire region. We model the characteristic of the region as a linear functional of Z(), a realization of a random field, and our data as the values of Z(x) for x along the path. We study how to estimate the characteristic of interest and how to choose the path to accurately estimate it.     

Robustness of Parameter Estimation Procedures in Multilevel Models When Random Effects are MEP Distributed

In this paper we examine maximum likelihood estimation procedures in multilevel models for two level nesting structures. Usually, for fixed effects and variance components estimation, level-one error terms and random effects are assumed to be normally distributed. Nevertheless, in some circumstances this assumption might not be realistic, especially as concerns random effects. Thus we assume for random effects the family of multivariate exponential power distributions (MEP); subsequently, by means of Monte Carlo simulation procedures, we study robustness of maximum likelihood estimators under normal assumption when, actually, random effects are MEP distributed.     

Improved estimation of the scale parameter, the hazard rate parameter and the ratio of the scale parameters in exponential distributions: An integrated approach

We give new classes of Strawderman-type improved estimators for the scale parameter σ₂ and the hazard rate parameter 1/σ₁ of the exponential distributions E(μ₂,σ₂) and E(μ₁,σ₁) under the entropy loss. We then use these estimators to construct improved estimators for the ratio ρ=σ₂/σ₁. The sampling framework we consider integrates important life-testing schemes separately studied in the literature so far, namely, (i) i.i.d. sampling, (ii) Type-II censoring, (iii) progressive Type-II censoring and adaptive progressive Type-II censoring and (iv) record values data. Furthermore, we establish simple identities connecting the risk functions of the estimators of σ₂ and 1/σ₁ and those of ρ that have a direct impact on studying the risk behavior of the latter estimators. Finally, we indicate that no matter which of the above life-testing schemes is employed for the estimation of σ₂, 1/σ₁ or ρ, the corresponding improved estimator, which may be of Stein-type or Brewster and Zidek-type or Strawderman-type, will offer the same improvement over the usual estimator as long as the number of observed complete failure times is the same for each scheme. Our results unify and extend existing results on the estimation of exponential scale parameters in one or two populations.     

Unit-Root Tests and the Statistical Pitfalls of Seasonal Adjustment: The Case of U.S. Postwar Real Gross National Product

An often-used scenario in marketing is that of individuals purchasing in a Poisson manner with their purchasing rates distributed gamma across the population of customers. Ehrenberg (1959) introduced the marketing community to this story and the resulting negative binomial distribution (NBD), and during the past 30 years the NBD model has been shown to work quite well. But the basic gamma/Poisson assumptions lack some face validity. In many product categories, customers purchase more regularly than the exponential. There are some individuals who will never purchase. The purpose of this article is to review briefly the literature that addresses these and other issues. The tractable results presented arise when the basic gamma/Poisson assumptions are relaxed one issue at a time. Some conjectures will be made about the robustness of the NBD when multiple deviations occur together. The NBD may work, but there are still opportunities for working on variations of the NBD theme.     

金融企业风险统计预警系统研究

金融企业运营风险监测、评估与预警是金融企业运营管理和风险控制的核心。本文分析了金融企业风险统计系统的发展状况,探讨了我国金融企业潜在的风险识别,结合我国金融企业发展现状,构造了可行的风险统计指标体系,设计了金融企业预警系统,并用于金融企业金融风险监测的实践。     

MFS核算体系的演变与启示

货币与金融统计（MFS）是宏观经济领域四大国际统计规范之一,其最新成果为国际货币基金组织将于2015年推出的《货币金融统计手册与编制指南》（MFSMCG）。本文着重从三个方面展开研究：首先,通过对MFS核算体系的发展历程进行梳理,就其所形成的趋势性特征进行归纳;其次,跟踪MFSMCG的进展情况,关注其与SNA等国际标准相互协调的外在要求,以及其为适应金融市场中所出现的各类新情况而不断修订的内在逻辑;最后,对照MFS国际标准来审视我国现有的货币与金融统计体系,并提出加快推进金融统计改革的建议。     

Statistical analysis of process capability indices with measurement errors: The case ofC p

Process capability indices (PCIs) have been widely used in manufacturing industries to previde a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement errors effects on the performance of PCIs. The analysis is illustrated with reference toC _p , i.e. the simplest and most common measure suggested to evaluate process capability. The authors would like to thank two anonymous referees for their comments and suggestion that were useful in the preparation and improvement of this paper. This work was partially supported by a MURST research grant.