论文检索

近似条件推断的贝叶斯方法AI 解读

Sweeting (1995) studies regular Bayesian and frequentist approximations within a unified framework in the case of a single parameter, and shows that higher-order approximations to sampling distributions arise from their Bayesian counterparts via an unsmoothing argument. In the present paper we extend this programme to include formulae in approximate conditional inference. In particular it is shown how Bayesian arguments may be used to derive some formulae developed by…

贝叶斯与似然近似的统一统计框架AI 解读

Regular Bayesian and frequentist approximations in statistics are studied within a unified framework. In particular it is shown how some higher-order likelihood-based approximations arise from their Bayesian counterparts via an unsmoothing argument. This approach serves to shed new light on these formulae and to clarify relationships between Bayesian and frequentist inferences. For example, Bayesian interpretations of higher-order approximations can give insights into the…

摘要暂缺

次序统计量之差的鞍点近似AI 解读

Saddlepoint approximations for the distribution of the difference of order statistics from a continuous distribution are presented. The approximations are illustrated for the case of the exponential distribution where we can find the exact probability density function of the difference of order statistics, and for the case of the noncentral chi-squared distribution which has application to the problem of testing a time series for linearity. In both cases we show that the…

相关失效时间数据中风险比参数的估计方程AI 解读

Weighted partial likelihood estimating equations are proposed for the estimation of marginal hazard ratio parameters based on correlated failure time data. Asymptotic distribution theory is derived for the solution to such equations using martingale convergence results and inverse function theory. Simulation studies and theoretical efficiency calculations indicate that the inclusion of weights in the estimating equation produces important efficiency gains only if the…

摘要暂缺

概率密度拉普拉斯变换反演的最大熵方法AI 解读

This papers presents a maximum entropy method for inverting Laplace transforms of density functions of positive random variables. The maximum entropy density is very flexible and can assume a variety of different shapes. Accurate approximations to the true density can be obtained even when only a few transform values are available. Numerical evidence is provided for gamma, lognormal, inverse Gaussian and Pareto distributions.

三处理三时期交叉设计的非参数分析AI 解读

SUMMARY In this paper, we present a nonparametric approach to test hypotheses concerning carryover effects and direct treatment effects in crossover designs consisting of replicated sets of two Williams squares of order three. The methodology is based on finding unbiased estimates of the effects within each replication and transforming the analysis of the original design into one for a randomised block design. Therefore the well-known rank tests for this latter design can be…

基于 p* 公式的简化预测密度AI 解读

The predictive density proposed by Harris (1989) is based on integrating the density for a new observation with respect to the estimated sampling distribution of the maximum likelihood estimator of the unknown parameter. This has good properties, but is rather complicated to compute even for simple models. An approximation to the Harris proposal is considered which consists of approximating the sampling distribution of the maximum likelihood estimator of the unknown…

多元二元响应的边际回归分析AI 解读

We propose the use of the mean parameter for regression analysis of a multivariate binary response. We model the association using dependence ratios defined in terms of the mean parameter, the components of which are the joint success probabilities of all orders. This permits flexible modelling of higher-order associations, using maximum likelihood estimation. We reanalyse two data sets, one with variable cluster size and the other a longitudinal data set with constant…

删失数据下变换模型的分析AI 解读

In this paper we consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linearly related to the covariates with various completely specified error distributions. This class of regression models includes the proportional hazards and proportional odds models. Inference procedures derived from a class of generalised estimating equations are proposed to examine the covariate effects with censored observations.…

依赖删失下的半参数回归估计AI 解读

We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured prior to start of follow-up, in the presence of dependent censoring given X. The proposed estimators are consistent when the data are ‘missing at random’ but not ‘missing completely at random’ (Rubin, 1976), and do not require full specification of the complete data likelihood.…

捕获—再捕获数据中迁移与存活概率的贝叶斯估计AI 解读

The Arnason–Schwarz model is usually used for estimating survival and movement probabilities of animal populations from capture-recapture data. The missing data structure of this capture-recapture model is exhibited and summarised via a directed graph representation. Taking advantage of this structure we implement a Gibbs sampling algorithm from which Bayesian estimates and credible intervals for survival and movement probabilities are derived. Convergence of the algorithm…

二元响应回归模型的贝叶斯残差分析AI 解读

In a binary response regression model, classical residuals are difficult to define and interpret due to the discrete nature of the response variable. In contrast, Bayesian residuals have continuous-valued posterior distributions which can be graphed to learn about outlying observations. Two definitions of Bayesian residuals are proposed for binary regression data. Plots of the posterior distributions of the basic ‘observed –fitted’ residuals can be helpful in outlier…

稳健回归模型选择的一般Akaike型准则AI 解读

Akaike's procedure (1970) for selecting a model minimises an estimate of the expected squared error in predicting new, independent observations. This selection criterion was designed for models fitted by least squares. A different model-fitting technique, such as least absolute deviation regression, requires an appropriate model selection procedure. This paper presents a general Akaike-type criterion applicable to a wide variety of loss functions for model fitting. It…

部分失效类型缺失时的竞争风险生存数据分析AI 解读

We propose a method to analyse competing risks survival data when failure types are missing for some individuals. Our approach is based on a standard proportional hazards structure for each of the failure types, and involves the solution to estimating equations. We present consistent and asymptotically normal estimators of the regression coefficients and related score tests. An appealing feature is that individuals with known failure types make the same contributions as they…

不完整更新过程数据的非参数最大似然估计AI 解读

The problem of estimating the lifetime distribution based on data from independently and identically distributed stationary renewal processes is addressed. The data are incomplete. A nonparametric maximum likelihood estimate of the Lifetime distribution is derived using the em algorithm. The missing information principle is used to estimate the standard error of the estimated distribution. The methodology is applied to a problem in the nursing profession where nurses…

区间删失观测下马尔可夫疾病—死亡过程的非参数估计AI 解读

The nonparametric estimation of the cumulative transition intensity functions in a threestate time-nonhomogeneous Markov process with irreversible transitions, an ‘illness-death’ model, is considered when times of the intermediate transition, e.g. onset of a disease, are interval-censored. The times of ‘death’ are assumed to be known exactly or to be right-censored. In addition the observed process may be left-truncated. Data of this type arise when the process is sampled…

论条件自回归与内蕴自回归AI 解读

Gaussian conditional autoregressions have been widely used in spatial statistics and Bayesian image analysis, where they are intended to describe interactions between random variables at fixed sites in Euclidean space. The main appeal of these distributions is in the Markovian interpretation of their full conditionals. Intrinsic autoregressions are limiting forms that retain the Markov property. Despite being improper, they can have advantages over the standard…

可逆跳跃马尔可夫链蒙特卡洛计算与贝叶斯模型判定AI 解读

Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not fixed. This paper proposes a new framework for the construction of reversible Markov chain samplers…

实证研究中的因果图AI 解读

The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for…

《基于加权残差的比例风险检验与诊断》AI 解读

M. PATRICIA, GRAMBSCH, M. TERRY, THERNEAU; ‘Proportional hazards tests and diagnostics based on weighted residuals’, Biometrika, Volume 82, Issue 3, 1 Sept

摘要暂缺

修正工具变量单位根检验的功效AI 解读

In this paper we investigate the power of Hall's (1989) instrumental variables tests for unit roots. A slight modification is made to Hall's t statistic so that the modified t statistic can be applied directly without encountering the possible negative standard error problem of Hall's original t statistic. The major finding is that the power curves of the instrumental variables tests are not monotonic functions of the autoregressive coefficient of the unit root models when…