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Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons

大规模多重比较中的零分布与非零效应比例估计

An important issue raised by Efron in the context of large-scale multiple comparisons is that in many applications, the usual assumption that the null distribution is known is incorrect, and seemingly negligible differences in the null may result in large differences in subsequent studies. This suggests that a careful study of estimation of the null is indispensable. In this article we consider the problem of estimating a null normal distribution, and a closely related…

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Robust Model-Based and Model-Assisted Predictors of the Finite Population Total

有限总体总量的稳健模型预测与模型辅助预测

The prediction approach to finite population inference has received considerable attention in recent years. Under this approach, the finite population is assumed to be a realization from a superpopulation described by a known probability model, usually a linear model. The prediction approach is often criticized for its lack of robustness against model misspecification. In this article we revisit this important issue and introduce a new robust prediction approach in which the…

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Partially Linear Hazard Regression for Multivariate Survival Data

多变量生存数据的部分线性风险回归

This article studies estimation of partially linear hazard regression models for multivariate survival data. A profile pseudo–partial likelihood estimation method is proposed under the marginal hazard model framework. The estimation on the parameters for the linear part is accomplished by maximization of a pseudo–partial likelihood profiled over the nonparametric part. This enables us to obtain -consistent estimators of the parametric component. Asymptotic normality is…

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Empirical Likelihood for a Varying Coefficient Model With Longitudinal Data

纵向数据变系数模型的经验似然推断

In this article local empirical likelihood-based inference for a varying coefficient model with longitudinal data is investigated. First, we show that the naive empirical likelihood ratio is asymptotically standard chi-squared when undersmoothing is employed. The ratio is self-scale invariant and the plug-in estimate of the limiting variance is not needed. Second, to enhance the performance of the ratio, mean-corrected and residual-adjusted empirical likelihood ratios are…

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Dynamic Integration of Time- and State-Domain Methods for Volatility Estimation

波动率估计中时间域与状态域方法的动态整合

Time- and state-domain methods are two common approaches to nonparametric prediction. Whereas the former uses data predominantly from recent history, the latter relies mainly on historical information. Combining these two pieces of valuable information is an interesting challenge in statistics. We surmount this problem by dynamically integrating information from both the time and state domains. The estimators from these two domains are optimally combined based on a…

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Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models

利用删失回归模型评估 t 年生存者预测规则

Suppose that we are interested in establishing simple but reliable rules for predicting future t-year survivors through censored regression models. In this article we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models, we derive consistent…

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Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function

协方差函数半参数估计下的纵向数据分析

Improving efficiency for regression coefficients and predicting trajectories of individuals are two important aspects in analysis of longitudinal data. Both involve estimation of the covariance function. Yet, challenges arise in estimating the covariance function of longitudinal data collected at irregular time points. A class of semiparametric models for the covariance function is proposed by imposing a parametric correlation structure while allowing a nonparametric…

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High-Dimension, Low–Sample Size Perspectives in Constrained Statistical Inference

约束统计推断中的高维小样本视角

High-dimensional categorical data models, often with inadequately large sample sizes, crop up in many fields of application. The SARS epidemic, originating in southern China in 2002, had an identified single-stranded and positive-sense RNA virus with large genome size and moderate mutation rate. The present genomic study is used as a prime illustration for motivating appropriate statistical methodology for comprehending the genomic variation in such high-dimensional…

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Pivotal Bootstrap Methods for k -Sample Problems in Directional Statistics and Shape Analysis

方向统计与形状分析中多样本问题的枢轴自助法

We propose a novel bootstrap hypothesis testing approach for the problem of testing a null hypothesis of a common mean direction, mean polar axis, or mean shape across several populations of real unit vectors (the directional case) or complex unit vectors (the two-dimensional shape case). Multisample testing problems of this type arise frequently in directional statistics and shape analysis (as in other areas of statistics), but to date there has been relatively little…

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Model Averaging and Dimension Selection for the Singular Value Decomposition

奇异值分解的模型平均与维度选择

Many multivariate data-analysis techniques for an m × n matrix Y are related to the model Y = M + E, where Y is an m × n matrix of full rank and M is an unobserved mean matrix of rank K < (m ∧ n). Typically the rank of M is estimated in a heuristic way and then the least-squares estimate of M is obtained via the singular value decomposition of Y, yielding an estimate that can have a very high variance. In this article we suggest a model-based alternative to the preceding…

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Smoothed Rank Regression With Censored Data

删失数据的平滑秩回归

A weighted rank estimating function is proposed to estimate the regression parameter vector in an accelerated failure time model with right censored data. In general, rank estimating functions are discontinuous in the regression parameter, creating difficulties in determining the asymptotic distribution of the estimator. A local distribution function is used to create a rank based estimating function that is continuous and monotone in the regression parameter vector. A…

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Nonparametric Tests for Perfect Judgment Rankings

判断排序完全正确的非参数检验

The ranked-set sampling literature includes both inference procedures that rely on the assumption of perfect rankings and inference procedures that are robust to violations of this assumption. Procedures that assume perfect rankings tend to be more efficient when rankings are in fact perfect, but they may be invalid when perfect rankings fail. As a result, users of ranked-set sampling must decide between efficiency and robustness, and there is at present little to guide…

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Disability and Employment

残疾与就业

Measurement error in health and disability status has been widely accepted as a central problem in social science research. Long-standing debates about the prevalence of disability, the role of health in labor market outcomes, and the influence of federal disability policy on declining employment rates have all emphasized issues regarding the reliability of self-reported disability. In addition to random error, inaccuracy in survey datasets may be produced by a host of…

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ON THE STATIONARITY OF MARKOV-SWITCHING GARCH PROCESSES

马尔可夫状态转换 GARCH 过程的平稳性

Generalized autoregressive conditional heteroskedasticity (GARCH) models with Markov-switching regimes are often used for volatility analysis of financial time series. Such models imply less persistence in the conditional variance than the standard GARCH model and potentially provide a significant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this paper, we introduce…

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A ROBUST BAYESIAN APPROACH FOR UNIT ROOT TESTING

单位根检验的稳健贝叶斯方法

In this paper we deal with the identification of an autoregressive model for an observed time series and the detection of a unit root in its characteristic polynomial. This is a big issue concerned with distinguishing stationary time series from time series for which differencing is required to induce stationarity. We consider a Bayesian approach, and particular attention is devoted to the problem of the sensitivity of the standard Bayesian analysis with respect to the…

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THE INTEGRATION ORDER OF VECTOR AUTOREGRESSIVE PROCESSES

向量自回归过程的单整阶数

We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2) conditions (Johansen, 1996, Likelihood-Based Inference in Cointegrated Vector Auto-Regressive Models) is proved.I am very grateful to Søren Johansen for his precious insights and his…

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AN EFFICIENT LINEAR GMM ESTIMATOR FOR THE COVARIANCE STATIONARY AR(1)/UNIT ROOT MODEL FOR PANEL DATA

协方差平稳与单位根面板 AR(1) 模型的有效线性 GMM 估计

This paper considers generalized method of moments (GMM) estimation of the inclusive panel AR(1) model that contains the covariance stationary panel AR(1) model and the panel AR(1) model with a unit root as special cases. The paper presents a two-step optimal linear GMM (OLGMM) estimator for the inclusive model that is asymptotically equivalent to the optimal nonlinear GMM estimator of Ahn and Schmidt (1997, Journal of Econometrics 76, 309–321) when the data are covariance…

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EFFICIENCY OF LINEAR ESTIMATORS UNDER HEAVY-TAILEDNESS: CONVOLUTIONS OF [alpha]-SYMMETRIC DISTRIBUTIONS

重尾下线性估计量的效率:α 对称分布的卷积

This paper focuses on the analysis of efficiency, peakedness, and majorization properties of linear estimators under heavy-tailedness assumptions. We demonstrate that peakedness and majorization properties of log-concavely distributed random samples continue to hold for convolutions of α-symmetric distributions with α > 1. However, these properties are reversed in the case of convolutions of α-symmetric distributions with α < 1.We show that the sample mean is the best linear…

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A MODEL SELECTION TEST FOR BIVARIATE FAILURE-TIME DATA

二元失效时间数据的模型选择检验

In this paper, we address two important issues in semiparametric survival model selection for censored data generated by the Archimedean copula family: method of estimating the parametric copulas and data reuse. We demonstrate that for selection among candidate copula models that might all be misspecified, estimators of the parametric copulas based on minimizing the selection criterion function may be preferred to other estimators. To handle the issue of data reuse, we put…

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Bayesian inference for the gravity model

引力模型的贝叶斯推断

Abstract This paper seeks to empirically extend the gravity model , which has been widely used to analyze volumes of trade between pairs of countries. We generalize the basic threshold tobit model by allowing for the inclusion of country‐specific effects into the analysis and also show how one can explore the relationship between trade volumes and a given covariate via a non‐parametric approach. We use our derived methodology to investigate the impact of a particular aspect…

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Efficient tests of long‐run causation in trivariate VAR processes with a rolling window study of the money–income relationship

三变量 VAR 过程长期因果关系的有效检验:货币—收入关系的滚动窗口研究

Abstract This paper develops a simple sequential multiple‐horizon non‐causation test strategy for trivariate VAR models (with one auxiliary variable). We apply the test strategy to a rolling window study of money supply and real income, with the price of oil, the unemployment rate and the spread between the Treasury bill and commercial paper rates as auxiliary processes. Ours is the first study to control simultaneously for common stochastic trends, sensitivity of test…

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Identifying technically efficient fishing vessels: a non‐empty, minimal subset approach

识别技术有效率的渔船:非空最小子集方法

Abstract Stochastic frontier models are often employed to estimate fishing vessel technical efficiency. Under certain assumptions, these models yield efficiency measures that are means of truncated normal distributions. We argue that these measures are flawed, and use the results of Horrace ( 2005 ) to estimate efficiency for 39 vessels in the Northeast Atlantic herring fleet, based on each vessel's probability of being efficient. We develop a subset selection technique to…

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Money demand function estimation by nonlinear cointegration

利用非线性协整估计货币需求函数

Abstract Conventionally, the money demand function is estimated using a regression of the logarithm of money demand on either the interest rate or the logarithm of the interest rate. This equation is presumed to be a cointegrating regression. In this paper, we aim to combine the logarithmic specification, which models the liquidity trap better than a linear model, with the assumption that the interest rate itself is an integrated process. The proposed technique is robust to…

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