论文检索

面板数据似然中得分偏差与信息偏差的作用AI 解读

We show why reducing information bias can improve the performance of likelihood based estimators and confidence regions in small samples, and why it seems to matter more for inference than for estimation. The insights in this paper are helpful in explaining several simulation findings in the panel data literature. E.g., we can explain the well documented phenomenon that reducing the score bias alone often reduces the finite sample variance of estimators and improves the…

含未观测效应的非线性面板模型的积分似然推断AI 解读

We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation…

内生解释变量非参数回归模型中线性泛函估计的效率界AI 解读

Let Y=μ∗(X)+ε, where μ∗ is unknown and E[ε|X]≠0 with positive probability but there exist instrumental variables W such that E[ε|W]=0 w.p.1. It is well known that such nonparametric regression models are generally “ill-posed” in the sense that the map from the data to μ∗ is not continuous. In this paper, we derive the efficiency bounds for estimating certain linear functionals of μ∗ without assuming μ∗ itself to be identified.

正则模型中修正带符号似然比统计量近似的高阶渐近正态性AI 解读

Approximations to the modified signed likelihood ratio statistic are asymptotically standard normal with error of order n−1, where n is the sample size. Proofs of this fact generally require that the sufficient statistic of the model be written as (θ̂, a), where θ̂ is the maximum likelihood estimator of the parameter θ of the model and a is an ancillary statistic. This condition is very difficult or impossible to verify for many models. However, calculation of the statistics…

含内生解释变量的非参数线性模型中的若干识别问题AI 解读

In applied work economists often seek to relate a given response variable y to some causal parameter μ* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of μ* as a conditional expectation breaks down if some or all of the explanatory variables…

摘要暂缺

基于积分似然的指数次序统计量模型推断AI 解读

Methods of statistical inference are developed for the exponential order statistic (EOS) model, where only a subset of order statistics from a collection of N iid exponential random detection times is observable. When the rate parameter for detections is unknown, the maximum likelihood estimator (MLE) of the unknown integer parameter N can be infinite with substantial probability. Inference for N is developed using a pseudolikelihood function obtained by integrating out the…

聚类数据的扩展广义估计方程AI 解读

Typically, analysis of data consisting of multiple observations on a cluster is complicated by within-cluster correlation. Estimating equations for generalized linear modeling of clustered data have recently received much attention. This article proposes an extension to the generalized estimating equation method proposed by Liang and Zeger, which treats within-cluster correlations as nuisance parameters. Using ideas from extended quasi-likelihood, estimating equations for…

聚类数据的扩展广义估计方程AI 解读

Typically, analysis of data consisting of multiple observations on a cluster is complicated by within-cluster correlation. Estimating equations for generalized linear modeling of clustered data have recently received much attention. This article proposes an extension to the generalized estimating equation method proposed by Liang and Zeger, which treats within-cluster correlations as nuisance parameters. Using ideas from extended quasi-likelihood, estimating equations for…

摘要暂缺

摘要暂缺

信息与条件推断AI 解读

Consider non-Bayesian inference about a parameter of interest β in the presence of a nuisance parameter λ based on a statistic S = (T, A). If the conditional distribution of T given A does not depend on λ and if A contains no information about β in the presence of λ, then A is said to be ancillary for θ in the presence of λ, and inference about θ is to be based on the conditional distribution of T given A. This approach has been the basis for several definitions of…

信息与条件推断AI 解读

Consider non-Bayesian inference about a parameter of interest β in the presence of a nuisance parameter λ based on a statistic S = (T, A). If the conditional distribution of T given A does not depend on λ and if A contains no information about β in the presence of λ, then A is said to be ancillary for θ in the presence of λ, and inference about θ is to be based on the conditional distribution of T given A. This approach has been the basis for several definitions of…

通过条件化近似消除干扰参数AI 解读

The general problem of inference about a scalar parameter of interest θ in the presence of a nuisance parameter λ using conditional inference is considered.xml . A condition is given under which inference based on the conditional distribution of θ^, the maximum likelihood estimate of θ, given λ^o, the maximum likelihood estimate of λ for fixed θ = θ0, is optimal, in a certain sense. When this condition is not satisfied, it is shown that inference should be based on the…

位置参数的非参数条件推断AI 解读

SUMMARY Let Y 1, ..., Yn denote independent observations of the form Yj = θ + σεj where ε 1, ..., εn are independent random variables each distributed according to a density p, and θ and σ are unknown parameters. This paper presents a method for conditional inference about the location parameter θ that does not require the specification of a parametric family of densities for p. This method uses the configuration statistic to select the density p and is based on the work of…

半参数模型中的拟似然估计AI 解读

Suppose the expected value of a response variable Y may be written h(Xβ +γ(T)) where X and T are covariates, each of which may be vector-valued, β is an unknown parameter vector, γ is an unknown smooth function, and h is a known function. In this article, we outline a method for estimating the parameter β, γ of this type of semiparametric model using a quasi-likelihood function. Algorithms for computing the estimates are given and the asymptotic distribution theory for the…

近似贝叶斯推断AI 解读

Consider statistical inference about a scalar parameter and suppose that information about that parameter is to be summarized by a system of interval estimates. It is well known that methods of interval estimation that do not correspond to Bayesian inference with respect to some prior distribution have some logical difficulties. This article proposes a measure of how close a system of interval estimates is to being of Bayesian form. Using this measure, several non-Bayesian…

Consider statistical inference about a scalar parameter and suppose that information about that parameter is to be summarized by a system of interval estimates. It is well known that methods of interval estimation that do not correspond to Bayesian inference with respect to some prior distribution have some logical difficulties. This article proposes a measure of how close a system of interval estimates is to being of Bayesian form. Using this measure, several non-Bayesian…

通过条件化近似消除干扰参数AI 解读

The general problem of inference about a scalar parameter of interest θ in the presence of a nuisance parameter λ using conditional inference is considered.xml . A condition is given under which inference based on the conditional distribution of θ^, the maximum likelihood estimate of θ, given λ^o, the maximum likelihood estimate of λ for fixed θ = θ0, is optimal, in a certain sense. When this condition is not satisfied, it is shown that inference should be based on the…

同时具有置信区间性质的贝叶斯区间估计AI 解读

SUMMARY Let Y 1, . . ., Yn denote independent observations each distributed according to a distribution depending on a scalar parameter θ suppose that we are interested in constructing an interval estimate for θ. One approach is to use Bayesian inference. For a given prior density, we can construct an interval such that the posterior probability that θ lies in the interval is some specified value. In this paper, a method is given for constructing a Bayesian interval estimate…

存在干扰参数时的局部辅助性AI 解读

Approximate ancillarity in the presence of a nuisance parameter is considered. A theory of approximate S-ancillarity, based on the concept of local ancillarity introduced by Cox (1980), is developed. First- and second-order local S-ancillarity are considered in detail and these results are applied to several examples. These examples indicate that, although first-order local S-ancillarity is a weak condition, second-order local S-ancillarity is too strong a requirement for…

剖面似然与条件参数模型AI 解读

In this paper, we outline a general approach to estimating the parametric component of a semiparametric model. For the case of a scalar parametric component, the method is based on the idea of first estimating a one-dimensional subproblem of the original problem that is least favorable in the sense of Stein. The likelihood function for the scalar parameter along this estimated subproblem may be viewed as a generalization of the profile likelihood for the problem. The scalar…

位置参数估计中的条件稳健性AI 解读

Let Y1,…, Yn denote independent real-valued observations, each distributed according to a density p(y − θ ), where θ is an unknown parameter and p is a symmetric density function. This paper considers robust point estimation of θ from the point of view of conditional inference. Specific measures of the conditional robustness of a location estimator are introduced, as well as measures of the conditional robustness of a particular sample. Location-scale models are also…

位置参数估计中的条件稳健性AI 解读

Let Y1,…, Yn denote independent real-valued observations, each distributed according to a density p(y − θ ), where θ is an unknown parameter and p is a symmetric density function. This paper considers robust point estimation of θ from the point of view of conditional inference. Specific measures of the conditional robustness of a location estimator are introduced, as well as measures of the conditional robustness of a particular sample. Location-scale models are also…