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非高斯状态空间模型的蒙特卡洛最大似然估计AI 解读

State space models are considered for observations which have non-Gaussian distributions. We obtain accurate approximations to the loglikelihood for such models by Monte Carlo simulation. Devices are introduced which improve the accuracy of the approximations and which increase computational efficiency. The loglikelihood function is maximised numerically to obtain estimates of the unknown hyperparameters. Standard errors of the estimates due to simulation are calculated.…

二元极值 Copula 的非参数估计方法AI 解读

A bivariate extreme value distribution with fixed marginals is generated by a one-dimensional map called a dependence function. This paper proposes a new nonparametric estimator of this function. Its asymptotic properties are examined, and its small-sample behaviour is compared to that of other rank-based and likelihood-based procedures. The new estimator is shown to be uniformly, strongly convergent and asymptotically unbiased. Through simulations, it is also seen to…

使用残差极大似然检验模型固定项的似然比方法AI 解读

Summary Likelihood ratio tests for fixed model terms are proposed for the analysis of linear mixed models when using residual maximum likelihood estimation. Bartlett-type adjustments, using an approximate decomposition of the data, are developed for the test statistics. A simulation study is used to compare properties of the test statistics proposed, with or without adjustment, with a Wald test. A proposed test statistic constructed by dropping fixed terms from the full…

Summary When measurement error is present in covariates, it is well known that naïvely fitting a generalized linear model results in inconsistent inferences. Several methods have been proposed to adjust for measurement error without making undue distributional assumptions about the unobserved true covariates. Stefanski and Carroll focused on an unbiased estimating function rather than a likelihood approach. Their estimating function, known as the conditional score, exists…

Summary In some applications it is cost efficient to sample data in two or more stages. In the first stage a simple random sample is drawn and then stratified according to some easily measured attribute. In each subsequent stage a random subset of previously selected units is sampled for more detailed and costly observation, with a unit’s sampling probability determined by its attributes as observed in the previous stages. This paper describes multistage sampling designs and…

利用拟 Newton 方法加速 EM 算法AI 解读

Summary The EM algorithm is a popular method for maximum likelihood estimation. Its simplicity in many applications and desirable convergence properties make it very attractive. Its sometimes slow convergence, however, has prompted researchers to propose methods to accelerate it. We review these methods, classifying them into three groups: pure, hybrid and EM-type accelerators. We propose a new pure and a new hybrid accelerator both based on quasi-Newton methods and…

带条件异方差的分数积分 ARMA 时间序列模型AI 解读

This article considers fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived.…

比例优势模型的最大似然估计AI 解读

We consider maximum likelihood estimation of the parameters in the proportional odds model with right-censored data. The estimator of the regression coefficient is shown to be asymptotically normal with efficient variance. The maximum likelihood estimator of the unknown monotonic transformation of the survival time converges uniformly at a parametric rate to the true transformation. Estimates for the standard errors of the estimated regression coefficients are obtained by…

区间删失比例优势失效时间回归模型的筛估计AI 解读

Estimation of the proportional-odds failure-time regression model with interval censoring is considered. Conditions that allow for positive information for the regression parameter are discussed. The efficient score is characterized by a Fredholm equation of the second kind. The sieve maximum likelihood estimator for the finite-dimensional regression parameter is shown to be asymptotically normal with √n convergence rate and to achieve the information bound. Data analysis…

删失数据非参数最大似然估计的混合计算算法AI 解读

We present a hybrid algorithm for nonparametric maximum likelihood estimation from censored data when the log-likelihood is concave. The hybrid algorithm uses a composite algorithmic mapping combining the expectation-maximization (EM) algorithm and the (modified) iterative convex minorant (ICM) algorithm. Global convergence of the hybrid algorithm is proven; the iterates generated by the hybrid algorithm are shown to converge to the nonparametric maximum likelihood estimator…

含无关数据与核验偏差样本的非参数极大似然估计AI 解读

Suppose that some measurements come from a distribution F that is of interest and others come from another, irrelevant distribution G. Some measurements from F are verified and known to be from F. The other, unverified measurements may be from F or from G. Not all measurements from F are equally likely to be verified, and no measurement from G is ever verified. This model applies to measurements of low concentrations obtained using gas chromatography/mass spectroscopy, for…

包含空间协变量的鱼类物种丰富度分析AI 解读

Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the area, elevation, and acidity of the lake. Data on these and other factors are available for 1,166 Adirondack lakes. The data are analyzed with the goal of quantifying the effects…

含无关数据与核验偏差样本的非参数最大似然估计AI 解读

Suppose that some measurements come from a distribution F that is of interest and others come from another, irrelevant distribution G. Some measurements from F are verified and known to be from F. The other, unverified measurements may be from F or from G. Not all measurements from F are equally likely to be verified, and no measurement from G is ever verified. This model applies to measurements of low concentrations obtained using gas chromatography/mass spectroscopy, for…

摘要暂缺

两阶段结果依赖抽样下逻辑回归参数的最大似然估计AI 解读

Summary Outcome-dependent sampling increases the efficiency of studies of rare outcomes, examples being case—control studies in epidemiology and choice–based sampling in econometrics. Two-phase or double sampling is a standard technique for drawing efficient stratified samples. We develop maximum likelihood estimation of logistic regression coefficients for a hybrid two-phase, outcome–dependent sampling design. An algorithm is given for determining the estimates by repeated…

重复测量剂量—反应关系的随机效应阈值模型AI 解读

Summary A random threshold model for dose--response designs with repeated measurements is introduced. Inference procedures for this model are discussed. The framework of generalized linear models is used to propose a quasi-likelihood inference procedure. The feasibility of this approach is illustrated with the analysis of food science data concerning a forced choice sensory experiment.

一种半参数最大似然估计量AI 解读

A maximum likelihood estimator for models containing nuisance parameters is proposed. The estimator is shown to be asymptotically normal and attain the semiparametric efficiency bounds for a number of important econometric models. The idea is to find a parametric model that passes through the true model. The score for the parametric model is then estimated nonparametrically and the estimator is obtained by setting the estimated score to zero.

过度识别广义矩模型的一步估计量AI 解读

In this paper I discuss alternatives to the GMM estimators proposed by Hansen (1982) and others. These estimators are shown to have a number of advantages. First of all, there is no need to estimate in an initial step a weight matrix as required in the conventional estimation procedure. Second, it is straightforward to derive the distribution of the estimator under general misspecification. Third, some of the alternative estimators have appealing information-theoretic…

信息不完整时的队列模型估计AI 解读

We consider the estimation of the arrival rate and holding distribution of a discrete time queue using only information about the size of the queue. Arrivals are not matched to departures so that direct observations of the holding times are not available. We study two estimators, quickly computed moment estimators and maximum likelihood estimators based on an entropy-motivated geometric approximation. This approximation lends the problem a Markovian structure that permits…

广义加权 Cramér–von Mises 距离估计量AI 解读

SUMMARY Generalised weighted Cramer-von Mises distance estimators in an arbitrary model with a k-dimensional parameter vector are investigated. The distance function is defined as a function G of model-based residuals for a specified target model F and the empirical cumulative distribution function F, over the real line, invoking a weight function w. It is shown that the estimator is Fisher consistent, asymptotically multivariate normal, and nearly efficient with desirable…

具有 t 分布误差的多元回归模型中 ECME 算法的收敛速度AI 解读

Although much work has been done on comparing and contrasting the EM and ECME algorithms, in terms of their rates of convergence, it is not clear what mechanism underlies each and, furthermore, what factors may determine and influence their rates of convergence. In this paper, we examine the convergence rates and properties of these two popular optimisation algorithms as used in computing the maximum likelihood estimates from regression models with t-distributed errors. By…

基于排序集样本的分布函数非参数最大似然估计的渐近性质AI 解读

We show that the nonparametric maximum likelihood estimator (NPMLE) of a distribution function based on balanced ranked set samples is consistent, converges weakly to a Gaussian process and is asymptotically efficient. The covariance function of the limiting process is described in terms of the solution to a Fredholm integral equation of the second kind.

部分线性模型中的惩罚准似然估计AI 解读

Consider a partial linear model, where the expectation of a random variable Y depends on covariates $(x, z)$ through $F(\theta_0 x + m_0(z))$, with $\theta_0$ an unknown parameter, and $m_0$ an unknown function. We apply the theory of empirical processes to derive the asymptotic properties of the penalized quasi-likelihood estimator.

无需光滑性假设的单峰密度估计AI 解读

The Grenander estimator of a decreasing density, which is defined as the derivative of the concave envelope of the empirical c.d.f., is known to be a very good estimator of an unknown decreasing density on the half-line $\mathbb{R}^+$ when this density is not assumed to be smooth. It is indeed the maximum likelihood estimator and one can get precise upper bounds for its risk when the loss is measured by the $\mathbb{L}^1$-distance between densities. Moreover, if one…