A simple procedure for the selection of significant effects

Summary.  Given a large number of test statistics, a small proportion of which represent departures from the relevant null hypothesis, a simple rule is given for choosing those statistics that are indicative of departure. It is based on fitting by moments a mixture model to the set of test statistics and then deriving an estimated likelihood ratio. Simulation suggests that the procedure has good properties when the departure from an overall null hypothesis is not too small.     

一个中国知识分子的仕士适——《顾准文集》读后

通过顾准的人生境遇分析中国知识分子为官、为儒的命运 ,提出 :官有官道 ,文有文道 ;当代中国知识分子有追求 ,更有窘迫 ;思想大师来自于苦难和冷清 ;思想让人成为人     

Gini’s nuclear family

The purpose of this paper is to justify the use of the Gini coefficient and two close relatives for summarizing the basic information of inequality in distributions of income. To this end we employ a specific transformation of the Lorenz curve, the scaled conditional mean curve, rather than the Lorenz curve as the basic formal representation of inequality in distributions of income. The scaled conditional mean curve is shown to possess several attractive properties as an alternative interpretation of the information content of the Lorenz curve and furthermore proves to yield essential information on polarization in the population. The paper also provides asymptotic distribution results for the empirical scaled conditional mean curve and the related family of empirical measures of inequality.      

论宪政生存的经济条件

自然经济和计划经济不可能产生宪政。市场经济是宪政产生和发展的前提和基础。市场经济产生权利观念、权利意识,促进政治法治化、民主化,要求限制政府权力,保护公民权利,产生宪法观念和宪法,培育宪政生存的社会基础———市民社会。     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

A Multivariate Model for Repeated Failure Time Measurements

A parametric multivariate failure time distribution is derived from a frailty-type model with a particular frailty distribution. It covers as special cases certain distributions which have been used for multivariate survival data in recent years. Some properties of the distribution are derived: its marginal and conditional distributions lie within the parametric family, and association between the component variates can be positive or, to a limited extent, negative. The simple closed form of the survivor function is useful for right-censored data, as occur commonly in survival analysis, and for calculating uniform residuals. Also featured is the distribution of ratios of paired failure times. The model is applied to data from the literature     

Curved Chaotic Map Time Series Models and Their Stochastic Reversals

This paper considers two types of chaotic map time series models, including the well-known tent, logistic and binary-shift maps as special cases; these are called curved tent and curved binary families. Deterministic behaviour is investigated by invariant distributions, Lyapunov exponents, and by serial dependency. Stochastic time reversal of the families is shown to produce models which have a broader range of stochastic and chaotic properties than their deterministic counterparts. The marginal distributions may have concentrations and restricted supports and are shown to be a non-standard class of invariant distribution. Dependenc y is generally weaker with the reversed stochastic models. The work gives a broad statistical account of deterministic and stochastically reversed map models, such as are emerging in random number generation, communica tion systems and cryptography     

De minimis and equity in risk

Indices and orderings are developed for evaluating alternative strategies in the management of risk. They reflect the goals of reducing individual and collective risks, of increasing equity, and of assigning priority to the reduction and to the equity of high risks. Individual risk is defined as the (random or non-random) level of exposure to a danger. In particular the role of a lower negligibility level is investigated. A class of indices is proposed which involves two parameters, a negligibility level and a parameter of inequality aversion, and several interpretations of the indices are discussed. We provide a set of eight axioms which are necessary and sufficient for this class of indices, and we present an approach to deal with partial information on the parameters.     

生存理性如何影响农户的差别化生产

在食品安全威胁下,农户重新定义了“为自家生产”和“为市场生产”的差别化生产行为。基于经济理性行为假设认为农户存在以确保自家食品安全为动机的生存理性。根据在5省区收集的827份农村入户调研数据,对农户差别化生产行为进行具体分析,描述了差别化生产行为总体特征,并测量农户食品安全自我保护意识、威胁感知程度和信息渠道等因素。运用Probit模型验证了命题：以食品安全自我保护意识为特征的生存理性,支配了农户为确保自家消费安全而进行的差别化生产。