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半参数模型：广义自洽方法AI 解读

Summary In semiparametric models, the dimension d of the maximum likelihood problem is potentially unlimited. Conventional estimation methods generally behave like O(d3). A new O(d) estimation procedure is proposed for a large class of semiparametric models. Potentially unlimited dimension is handled in a numerically efficient way through a Nelson–Aalen-like estimator. Discussion of the new method is put in the context of recently developed minorization–maximization…

蒙特卡洛积分的统计模型理论AI 解读

Summary The task of estimating an integral by Monte Carlo methods is formulated as a statistical model using simulated observations as data. The difficulty in this exercise is that we ordinarily have at our disposal all of the information required to compute integrals exactly by calculus or numerical integration, but we choose to ignore some of the information for simplicity or computational feasibility. Our proposal is to use a semiparametric statistical model that makes…

M 估计量与正态分位数AI 解读

This paper explores a class of robust estimators of normal quantiles filling the gap between maximum likelihood estimators and empirical quantiles. Our estimators are linear combinations of M-estimators. Their asymptotic variances can be arbitrarily close to variances of the maximum likelihood estimators. Compared with empirical quantiles, the new estimators offer considerable reduction of variance at near normal probability distributions.

结果依赖两阶段抽样下半参数回归的大样本理论AI 解读

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood…

具有 GARCH(1,1) 误差的单位根过程渐近推断AI 解读

This paper investigates the so-called one-step local quasi–maximum likelihood estimator for the unit root process with GARCH(1,1) errors. When the scaled conditional errors (the ratio of the disturbance to the conditional standard deviation) follow a symmetric distribution, the asymptotic distribution of the estimated unit root is derived only under the second-order moment condition. It is shown that this distribution is a functional of a bivariate Brownian motion as in Ling…

动态面板数据估计量的时间维度与截面维度渐近性质AI 解读

In this paper we derive the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N→ 0 the fixed T results for GMM and LIML remain valid, but WG, although consistent, has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM, and LIML estimators…

基于月度与季度序列的新经济周期同步指数AI 解读

Abstract Popular monthly coincident indices of business cycles, e.g. the composite index and the Stock–Watson coincident index, have two shortcomings. First, they ignore information contained in quarterly indicators such as real GDP. Second, they lack economic interpretation; hence the heights of peaks and the depths of troughs depend on the choice of an index. This paper extends the Stock–Watson coincident index by applying maximum likelihood factor analysis to a…

利用合并检测估计有序二项比例AI 解读

This paper considers group testing when the probability of response is increasing across the levels of an observed covariate. We illustrate how previously known results in order‐restricted inference can be extended to situations wherein data are collected according to a group‐testing protocol, and we derive maximum likelihood estimators for proportions under the increasing order restriction and group‐testing model. Finally, we show how the use of group testing can…

纵向计数数据的序列相关伽马脆弱性模型AI 解读

A Poisson‐gamma model is introduced to account for between‐subjects heterogeneity and within‐subjects serial correlation occurring in longitudinal count data. The model extends the usual time‐constant shared frailty approach to allow time‐varying serially correlated gamma frailty whilst retaining standard marginal assumptions. A composite likelihood approach to estimation and testing for serial correlation is proposed. The work is motivated by a clinical trial on…

分量参数的似然AI 解读

For a statistical model with data, likelihood for the scalar or vector full parameter θ, of dimension p say, is typically well defined and easily computed. In this paper, we investigate likelihood for a component parameter ψ(θ) of dimension d < p and make use of the recent likelihood theory that has been successful in producing highly accurate third‐order p‐values for scalar parameters of continuous models. The theory leads under moderate regularity to a definitive…

污染情形下最小对比估计量的中偏差AI 解读

Since statistical models are simplifications of reality, it is important in estimation theory to study the behavior of estimators also under distributions (slightly) different from the proposed model. In testing theory, when dealing with test statistics where nuisance parameters are estimated, knowledge of the behavior of the estimators of the nuisance parameters is needed under alternatives to evaluate the power. In this paper the moderate deviation behavior of minimum…

不可识别模型与多层神经网络的似然比AI 解读

This paper discusses the behavior of the maximum likelihood estimator (MLE), in the case that the true parameter cannot be identified uniquely. Among many statistical models with unidentifiability, neural network models are the main concern of this paper. It is known in some models with unidentifiability that the asymptotics of the likelihood ratio of the MLE has an unusually larger order. Using the framework of locally conic models put forth by Dacunha-Castelle and Gassiat…

未记录有效投票选票的重新计票AI 解读

The vote recount in the 2000 Presidential election (Broward, Miami-Dade and Palm Beach Counties, Florida) is examined for evidence of bias. A precinct-level dataset is constructed, incorporating the machine-vote tally, the recount vote tally, voter registration demographics, and the ballot review by media sources. A new multivariate beta-logit model is introduced that allows joint modeling of multivariate unobserved latent probabilities. A simple two-step estimator is…

使用阿基米德 Copula 建模样本选择AI 解读

By a theorem due to Sklar, a multivariate distribution can be represented in terms of its underlying margins by binding them together using a copula function. By exploiting this representation, the "copula approach" to modelling proceeds by specifying distributions for each margin and a copula function. In this paper, a number of families of copula functions are given, with attention focusing on those that fall within the Archimedean class. Members of this class of copulas…

具有平滑随机效应的函数型准似然回归模型AI 解读

Summary We propose a class of semiparametric functional regression models to describe the influence of vector-valued covariates on a sample of response curves. Each observed curve is viewed as the realization of a random process, composed of an overall mean function and random components. The finite dimensional covariates influence the random components of the eigenfunction expansion through single-index models that include unknown smooth link and variance functions. The…

部分区间删失数据下比例风险模型的最大似然估计AI 解读

Summary The maximum likelihood estimator (MLE) for the proportional hazards model with partly interval-censored data is studied. Under appropriate regularity conditions, the MLEs of the regression parameter and the cumulative hazard function are shown to be consistent and asymptotically normal. Two methods to estimate the variance–covariance matrix of the MLE of the regression parameter are considered, based on a generalized missing information principle and on a generalized…

摘要暂缺

有限混合分布、序贯似然与EM算法AI 解读

A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log-likelihood function. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate…

平稳高斯强相依过程参数最大似然估计的有效渐近展开AI 解读

We establish the validity of an Edgeworth expansion to the distribution of the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process. The result covers ARFIMA-type models, including fractional Gaussian noise. The method of proof consists of three main ingredients: (i) verification of a suitably modified version of Durbin's general conditions for the validity of the Edgeworth expansion to the joint density of the log-likelihood…

向量 ARMA-GARCH 模型的渐近理论AI 解读

This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. The conditions for the strict stationarity, the ergodicity, and the higher order moments of the model are established. Consistency of the quasi-maximum-likelihood estimator (QMLE) is proved under only the second-order moment condition. This consistency result is new, even for the univariate autoregressive…

竞争风险下现状数据的非参数估计AI 解读

A great deal of recent attention has focused on the estimation of survival distributions based on current status data, an extreme form of interval censored data. This particular data structure arises in a wide variety of applications where cross‐sectional observation either naturally occurs or is preferred to more traditional forms of follow‐up. Here we consider current status data in the context of competing risks. We briefly consider simple parametric models as a backdrop…

非线性混合效应模型中的非参数估计AI 解读

A nonparametric approach is developed herein to estimate parameters in nonlinear mixed effects models. Asymptotic properties of the nonparametric maximum likelihood estimators and associated computational algorithms are provided. Empirical Bayes estimators of functionals of the random effects are also developed. Applications to population pharmacokinetics are given.

聚类生存数据中脆弱性测量误差模型的 SIMEX 函数型推断AI 解读

We consider frailty models for clustered survival data in the presence of measurement errors in covariates. We first show that when the measurement error is accounted for in a full likelihood analysis but the distribution of the unobserved covariate is misspecified, the maximum likelihood estimators are asymptotically biased, especially for the variance component, whose bias can be substantial. We then discuss making inference using functional estimation via the SIMEX method…

两阶段抽样回归问题的伪得分估计量AI 解读

Two-phase stratified sampling designs yield efficient estimates of population parameters in regression models while minimizing the costs of data collection. In measurement error problems, for example, error-free covariates are ascertained only for units selected in a validation sample. Estimators proposed heretofore for such designs require all units to have positive probability of being selected. We describe a new semiparametric estimator that relaxes this assumption and…