论文检索

统计推断工具：观测数据与数据增广方法AI 解读

Observed data techniques - normal approximation observed data techniques the EM algorithm data augmentation the Gibbs sampler.

计数过程与生存分析AI 解读

Preface. 0. The Applied Setting. 1. The Counting Process and Martingale Framework. 2. Local Square Integrable Martingales. 3. Finite Sample Moments and Large Sample Consistency of Tests and Estimators. 4. Censored Data Regression Models and Their Application. 5. Martingale Central Limit Theorem. 6. Large Sample results of the Kaplan-Meier Estimator. 7. Weighted Logrank Statistics. 8. Distribution Theory for Proportional Hazards Regression. Appendix A: Some Results from…

摘要暂缺

解释变量缺失时的回归：综述AI 解读

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive.We use information technology and tools to increase productivity and facilitate new forms of scholarship.

线性生灭过程的性质刻画AI 解读

This research determined the manner of convergence of certain Markov processes to their steady state limiting distributions. This article looks at linear birth and death processes with birth rate at each state determined by the immigration constant a and the natural growth multiplier b; with death rate at each state determined by fixed execution constant c and the natural declination multiplier d. All parameters are nonnegative. There is a reflective barrier at state 0. It…

具有二次方差函数的指数族共轭先验AI 解读

Consider a natural exponential family parameterized by θ. It is well known that the standard conjugate prior on θ is characterized by a condition of posterior linearity for the expectation of the model mean parameter μ. Often, however, this family is not parameterized in terms of θ but rather in terms of a more usual parameter, such at the mean μ. The main question we address is: Under what conditions does a standard conjugate prior on μ induce a linear posterior expectation…

删失数据下尺度参数的最小距离估计AI 解读

This article examines how the level of censoring affects the performance of estimators of scale that minimize a weighted Cramérvon Mises distance between the Kaplan-Meier product-limit estimator of the survival distribution function and an assumed model. The article shows that under certain conditions these estimators are asymptotically normal if the survival function is a member of the assumed scale family. Weights that minimize the asymptotic variance under these…

多终点下关于指定处理的假设检验AI 解读

The problem of comparing an identified treatment with K other treatments is considered in a multivariate setting. Many formulations of composite alternative hypotheses are possible. For example, one might wish to examine whether the identified treatment is superior to the other treatments on all components of the response vector (i.e., is uniformly best) or whether the identified treatment is better than each treatment on at least one component (i.e., is admissible). For…

多元 Probit 模型最大似然估计的存在性与唯一性AI 解读

The multivariate probit model (MPM) is a particular case of the class of correlated prediction models. A correlated prediction model is especially useful when prediction or classification is envisaged into diagnostic classes that are combinations of binary responses. The parameter vector consists of a “location” part and an “association” part. The location part accounts for the effect the regressors have on the marginal probabilities of the binary responses. The association…

估计矩阵的秩与定性检验：因子、状态空间及 ARMA 应用AI 解读

Consider any consistent, asymptotically normal estimate ǎ of an arbitrary rectangular or square matrix A. This article derives an explicit test for the rank of A and a related test of (semi) definiteness of A. Potential applications include testing for identification of structural models, testing for the number of state variables in state-space models (including tests for the order of autoregressive moving average (ARMA) processes), consumer demand analysis applications, and…

复杂调查数据的重抽样程序AI 解读

In complex survey data, often the sampling design induces a non-iid structure to the data (e.g., without replacement sampling, stratification, multistage, or unequal probability of selection). Though techniques for variance estimation and confidence intervals do exist, they often are cumbersome to implement or do not extend to complex designs. It would be desirable to have resampling methods that reuse the existing estimation system repeatedly, using computing power to avoid…

Little Bootstrap 与回归维度选择：固定设计下的预测误差AI 解读

When a regression problem contains many predictor variables, it is rarely wise to try to fit the data by means of a least squares regression on all of the predictor variables. Usually, a regression equation based on a few variables will be more accurate and certainly simpler. There are various methods for picking “good” subsets of variables, and programs that do such procedures are part of every widely used statistical package. The most common methods are based on stepwise…

模型选择准则的分布性质AI 解读

It is commonly accepted that statistical modeling should follow the parsimony principle; namely, that simple models should be given priority whenever possible. But little quantitative knowledge is known concerning the amount of penalty (for complexity) regarded as allowable. We try to understand the parsimony principle in the context of model selection. In particular, the generalized final prediction error criterion is considered, and we argue that the penalty term should be…

多元动态线性变量误差模型的可识别性AI 解读

This article considers multivariate causal transfer function systems with latent stationary inputs and outputs. Their observation is assumed to be disturbed by errors in variables (EV). The main identification results for such models so far consist of structure theory. Derived are descriptions of classes of EV transfer function systems compatible with given covariance structures of the observed variables under various causality constraints. The case of unique determination…

用非参数回归估计混沌系统的李雅普诺夫指数AI 解读

We discuss procedures based on nonparametric regression for estimating the dominant Lyapunov Exponent λ1 from time series data generated by a nonlinear autoregressive system with additive noise. For systems with bounded fluctuations, λ1 > 0 is the defining feature of chaos. Thus our procedures can be used to examine time series data for evidence of chaotic dynamics. We show that a consistent estimator of the partial derivatives of the autoregression function can be used to…

非独立同分布数据下弹着精度分位数的可行贝叶斯估计AI 解读

An important measure of accuracy for problems of directing projectiles at targets is the circular error probable (CEP), a bivariate version of a 50% quantile point. This article presents a Bayesian procedure for estimating CEP when the projectile impact measurements are not iid, which is the case of usual practical interest. Our interest in a Bayesian procedure is motivated by a desire to combine accuracy information from several different sources. Except for the simplest…

聚类离散与连续结局的双变量潜变量模型AI 解读

We use the concept of a latent variable to derive the joint distribution of a continuous and a discrete outcome, and then extend the model to allow for clustered data. The model can be parameterized in a way that allows one to write the joint distribution as a product of a standard random effects model for the continuous variable and a correlated probit model for the discrete variable. This factorization suggests a convenient approach to parameter estimation using…

利用纵向CD4 T细胞数量研究HIV感染进展的层级贝叶斯模型AI 解读

Taking the absolute number of CD4 T-cells as a marker of disease progression for persons infected with the human immunodeficiency virus (HIV), we model longitudinal series of such counts for a sample of 327 subjects in the San Francisco Men's Health Study (Waves 1–8, excluding zidovudine cases). We conduct a fully Bayesian analysis of these data. We employ individual level nonlinear models incorporating such critical features as incomplete and unbalanced data, population…

贝叶斯与非贝叶斯方法的折中：简要综述AI 解读

Various compromises that have occurred between Bayesian and non-Bayesian methods are reviewed. (A citation is provided that discusses the inevitability of compromises within the Bayesian approach.) One example deals with the masses of elementary particles, but no knowledge of physics will be assumed.

双因素混合模型的秩方法AI 解读

The test problem of fixed treatment effects is considered in the two-factor mixed model with interaction and unequal cell frequencies when the classical assumptions of normality do not hold. An explicit form of a test statistic is derived using a partial rank transform (ranking all observations within each block), and the asymptotic distribution of the statistic is determined under the assumption that the number of blocks tends to infinity and the cell frequencies are…

分位数过程弱收敛的初等方法及其在删失生存数据中的应用AI 解读

Let ξ be a continuously differentiable function with positive derivative, and let ξ n be a sequence of right-continuous increasing processes. We show that if n 1/2(ξ n − ξ) W, where W is continuous, then n 1/2(ξ−1 n − ξ−1) − W(ξ−1)/ξ′(ξ−1). This result is applied to classical processes such as the empirical distribution function, the Kaplan-Meier estimator, and some other situations. We also prove an analogous result for the bootstrapped version of n 1/2(ξ n − ξ) and show…

改进 Gibbs 抽样：Gibbs Stopper 与 Griddy-Gibbs 方法AI 解读

The article briefly reviews the history, literature, and form of the Gibbs sampler. An importance sampling device is proposed for converting the output of the Gibbs sampler to a sample from the exact posterior. This Gibbs stopper technique is also useful for assessing convergence of the Gibbs sampler for moderate sized problems. Also presented is an approach for implementing the Gibbs sampler in nonconjugate situations. The basic idea is to approximate the true cdf of each…

利用条件密度混合估计层级模型边际后验密度AI 解读

A technique called quantile integration is proposed for the estimation of marginal posterior densities arising in Bayesian models having hierarchical representations. The method is based on approximating marginal densities as mixtures of conditional densities, where the conditioning variables are selected deterministically from the mixing distributions. The form of the approximation makes it easy to implement, and the resulting approximations are computationally efficient to…

条件 Logit 的可推广表述与诊断方法AI 解读

The conditional logit model is a multinomial logit model that permits the inclusion of choice-specific attributes. This article shows that the conditional logit model will maximize entropy given a set of attribute-value preserving constraints. A correspondence between the maximum entropy (ME) and maximum likelihood (ML) estimates for logit probabilities is established. Some easily computable and useful diagnostics for logit analysis are provided, and it is shown that an…