Bayesian inference for categorical data analysis

This article surveys Bayesian methods for categorical data analysis, with primary emphasis on contingency table analysis. Early innovations were proposed by Good (1953, 1956, 1965) for smoothing proportions in contingency tables and by Lindley (1964) for inference about odds ratios. These approaches primarily used conjugate beta and Dirichlet priors. Altham (1969, 1971) presented Bayesian analogs of small-sample frequentist tests for 2 x 2 tables using such priors. An alternative approach using normal priors for logits received considerable attention in the 1970s by Leonard and others (e.g., Leonard 1972). Adopted usually in a hierarchical form, the logit-normal approach allows greater flexibility and scope for generalization. The 1970s also saw considerable interest in loglinear modeling. The advent of modern computational methods since the mid-1980s has led to a growing literature on fully Bayesian analyses with models for categorical data, with main emphasis on generalized linear models such as logistic regression for binary and multi-category response variables.     

DISPERSION CONTROL FOR MULTIVARIATE PROCESSES

This paper presents some techniques for monitoring and controlling the dispersion of multivariate normal processes based on subgroup data. The procedures involve use of independent statistics resulting from the decomposition of the covariance matrix. Those that do not depend on prior estimates of the process covariance matrix are particularly attractive to short-run or low volume manufacturing environments.     

First-passage densities of controlled Gaussian processes

Whittle has proved a theorem that gives the optimal control of Gaussian processes in terms of the mathematical expectation of a function of the time and the place where the uncontrolled processes hit the boundary of the stopping region for the first time. In this paper we obtain formulae for the joint probability density function of the first hitting time and place and, in the time-invariant case, for the moment generating function of the first exit time of the optimally controlled processes. Two particular one-dimensional cases are considered.     

西方新政治经济学与东亚的现实

文章对照中国和韩国等东亚的传统和现实 ,对西方新政治经济学进行方法论分析 ,主要分析公共选择理论的三个理论假设和新制度经济学的制度变迁理论。心理学、社会学、哲学、伦理学等相关学科都对经济人假设提出了批评。东亚经济政治的制度结构与西方不同 ,经济人假设的适用性受到限制。政治与市场有本质的区别 ,交换政治观本身就有问题。有权威主义传统的东亚国家与西方国家不同 ,在儒家“行仁政”思想的影响下 ,政府对经济的干预程度深 ,政治中“交换”的色彩不明显。方法论的个人主义逻辑上仍有缺陷 ,在强调国和家大于个人 ,注重社群伦理的东亚 ,个人主义的地位并不突出。此外东亚的个人主义的内涵也不同于西方 ,有非理性的特征。政治具有报酬递增和路径依赖的特征 ,西方和中国的学者使用新制度经济学的方法分析中国政治和改革 ,各自取得了积极的成果。路径依赖理论较为适于中国的改革分析。     

Transformative Processes in Marriage: An Analysis of Emerging Trends

The study of conflict has dominated psychological research on marriage. This article documents its move from center stage, outlining how a broader canvas accommodates a richer picture of marriage. A brief sampling of new constructs such as forgiveness and sacrifice points to an organizing theme of transformative processes in emerging marital research. The implications of marital transformations are explored including spontaneous remission of distress, nonlinear dynamic systems that may produce unexpected and discontinuous change, possible nonarbitrary definitions of marital discord, and the potential for developing other constructs related to self‐transformation in marital research.     

Conditional inference under simultaneous stochastic ordering constraints

Testing for stochastic ordering is of considerable importance when increasing does of a treatment are being compared, but in applications involving multivariate responses has received much less attention. We propose a permutation test for testing against multivariate stochastic ordering. This test is distribution-free and no assumption is made about the dependence relations among variables. A comparative simulation study shows that the proposed solution exhibits a good overall performance when compared with existing tests that can be used for the same problem.     

Realized Volatility: A Review

This article reviews the exciting and rapidly expanding literature on realized volatility. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results. A continuous time specification provides the theoretical foundation for the main results in this literature. Cases with and without microstructure noise are considered, and it is shown how microstructure noise can cause severe problems in terms of consistent estimation of the daily realized volatility. Independent and dependent noise processes are examined. The most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given. The finite sample properties are discussed in comparison with their asymptotic properties. A multivariate model is presented to discuss estimation of the realized covariances. Various issues relating to modelling and forecasting realized volatilities are considered. The main empirical findings using univariate and multivariate methods are summarized.     

Longitudinal data analysis for generalized linear models with follow‐up dependent on outcome‐related variables

In longitudinal studies, observation times are often irregular and subject‐specific. Frequently they are related to the outcome measure or other variables that are associated with the outcome measure but undesirable to condition upon in the model for outcome. Regression analyses that are unadjusted for outcome‐dependent follow‐up then yield biased estimates. The authors propose a class of inverse‐intensity rate‐ratio weighted estimators in generalized linear models that adjust for outcome‐dependent follow‐up. The estimators, based on estimating equations, are very simple and easily computed; they can be used under mixtures of continuous and discrete observation times. The predictors of observation times can be past observed outcomes, cumulative values of outcome‐model covariates and other factors associated with the outcome. The authors validate their approach through simulations and they illustrate it using data from a supported housing program from the US federal government.     

RECENT TRENDS IN ASYMPTOTIC OPTIMAL INFERENCE FOR DEPENDENT OBSERVATIONS1

An overview of some recent developments in the area of asymptotic inference for non-ergodic type stochastic processes is presented. Both local and global formulations of the asymptotic model are given, and non-local optimality results are reviewed. Recent results on conditional inference are briefly discussed. Some open problems and possibilities for new developments are also mentioned.     

An Additive–Multiplicative Cox–Aalen Regression Model

We propose an additive–multiplicative intensity model that extends the Cox regression model as well as the additive Aalen model. Instead of having a simple baseline intensity the extended model uses an additive Aalen model as its covariate dependent baseline. Approximate maximum likelihood estimators of the baseline intensity functions and the relative risk parameters of the Cox model are suggested by solving the score equations. The derived estimator is efficient. We establish the large sample properties of the estimator. The model provides a simple pragmatic way of including time-varying covariate effects.