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未知误差分布下函数型测量误差模型的半参数估计AI 解读

Summary We consider functional measurement error models where the measurement error distribution is estimated non-parametrically. We derive a locally efficient semiparametric estimator but propose not to implement it owing to its numerical complexity. Instead, a plug-in estimator is proposed, where the measurement error distribution is estimated through non-parametric kernel methods based on multiple measurements. The root n consistency and asymptotic normality of the…

聚类数据的自助法AI 解读

Summary Various bootstraps have been proposed for bootstrapping clustered data from one-way arrays. The simulation results in the literature suggest that some of these methods work quite well in practice; the theoretical results are limited and more mixed in their conclusions. For example, McCullagh reached negative conclusions about the use of non-parametric bootstraps for one-way arrays. The purpose of this paper is to extend our understanding of the issues by discussing…

按出现顺序标记的隐马尔可夫模型：推断与模型选择AI 解读

Summary The system equation of a hidden Markov model is rewritten to label the components by order of appearance, and to make explicit the random behaviour of the number of components, mt. We argue that this reformulation is often a good way to achieve identifiability, as it facilitates the interpretation of the posterior density, and the estimation of the number of components that have appeared in a given sample. We develop a sequential Monte Carlo algorithm for estimating…

区分非正态模型的最优实验设计准则AI 解读

Summary Typically T-optimality is used to obtain optimal designs to discriminate between homoscedastic models with normally distributed observations. Some extensions of this criterion have been made for the heteroscedastic case and binary response models in the literature. In this paper, a new criterion based on the Kullback–Leibler distance is proposed to discriminate between rival models with non-normally distributed observations. The criterion is coherent with the…

多元极值的混合模型AI 解读

Summary The spectral density function plays a key role in fitting the tail of multivariate extre-mal data and so in estimating probabilities of rare events. This function satisfies moment con-straints but unlike the univariate extreme value distributions has no simple parametric form. Parameterized subfamilies of spectral densities have been suggested for use in applications, and non-parametric estimation procedures have been proposed, but semiparametric models for…

混合分布非参数最大似然估计的快速计算AI 解读

Summary A fast algorithm for computing the non-parametric maximum likelihood estimate of a mixing distribution is presented. At each iteration, the algorithm adds new important points to the support set as guided by the gradient function, updates all mixing proportions via a quadratically convergent method and discards redundant support points straightaway. With its convergence being theoretically established, numerical studies show that it is very fast and stable, compared…

贝叶斯密度回归AI 解读

Summary The paper considers Bayesian methods for density regression, allowing a random probability distribution to change flexibly with multiple predictors. The conditional response distribution is expressed as a non-parametric mixture of regression models, with the mixture distribution changing with predictors. A class of weighted mixture of Dirichlet process priors is proposed for the uncountable collection of mixture distributions. It is shown that this specification…

两阶段抽样遗传流行病研究中条件与边际混合设定回归的最大似然推断AI 解读

Summary Two-phase stratified sampling designs can reduce the cost of genetic epidemiologic studies by limiting expensive ascertainments of genetic and environmental exposure to an efficiently selected subsample (phase II) of the main study (phase I). Family history and some covariate information, which may be cheaply gathered for all subjects at phase I, can be used for sampling of informative subjects at phase II. We develop alternative maximum likelihood methods for…

概率预测、校准与集中度AI 解读

Summary Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the…

非负Garrotte估计量研究AI 解读

Summary We study the non-negative garrotte estimator from three different aspects: consistency, computation and flexibility. We argue that the non-negative garrotte is a general procedure that can be used in combination with estimators other than the original least squares estimator as in its original form. In particular, we consider using the lasso, the elastic net and ridge regression along with ordinary least squares as the initial estimate in the non-negative garrotte.…

缺失响应问题中的经验似然推断及其观察性研究应用AI 解读

Summary The problem of missing response data is ubiquitous in medical and social science studies. In the case of responses that are missing at random (depending on some covariate information), analyses focused only on the complete data may lead to biased results. Various debias methods have been extensively studied in the literature, particularly the weighting method that was motivated by Horvitz and Thompson's estimators. To improve efficiency, Robins, Rotnitzky and Zhao…

含时变协变量纵向数据的边际回归分析：广义矩方法AI 解读

Summary We develop a new approach to using estimating equations to estimate marginal regression models for longitudinal data with time-dependent covariates. Our approach classifies time-dependent covariates into three types—types I, II and III. The type of covariate determines what estimating equations can be used involving the covariate. We use the generalized method of moments to make optimal use of the estimating equations that are made available by the covariates.…

利用 lasso 收缩与选择回归系数及自回归阶数AI 解读

Summary The least absolute shrinkage and selection operator (‘lasso’) has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors. Two types of lasso estimators are carefully studied. The first is similar to the traditional lasso estimator with only two tuning parameters (one for regression coefficients and the other for autoregression coefficients). These tuning parameters can be easily calculated…

平稳空间点过程的最小二乘拟合AI 解读

Summary The K-function is a popular tool for fitting spatial point process models owing to its simplicity and wide applicability. In this work we study the properties of least squares estimators of model parameters and propose a new method of model fitting via the K-function by using subsampling. We demonstrate consistency and asymptotic normality of our estimators of model parameters and compare the efficiency of our procedure with existing procedures. This is done through…

构建稠密可变形模板估计的统一统计框架AI 解读

Summary The problem of estimating probabilistic deformable template models in the field of computer vision or of probabilistic atlases in the field of computational anatomy has not yet received a coherent statistical formulation and remains a challenge. We provide a careful definition and analysis of a well-defined statistical model based on dense deformable templates for grey level images of deformable objects. We propose a rigorous Bayesian framework for which we prove…

用于时间序列预测的函数型小波—核方法AI 解读

Summary We consider the prediction problem of a time series on a whole time interval in terms of its past. The approach that we adopt is based on functional kernel nonparametric regression estimation techniques where observations are discrete recordings of segments of an underlying stochastic process considered as curves. These curves are assumed to lie within the space of continuous functions, and the discretized time series data set consists of a relatively small, compared…

存在不依从的三臂试验中的因果效应界AI 解读

Summary The paper considers the analysis of three-arm randomized trials with non-compliance. In these trials, the average causal effects of treatments within principal strata of compliance behaviour are of interest for better understanding the effect of the treatment. Unfortunately, even with the usual assumptions, the average causal effects of treatments within principal strata are not point identified. However, the observable data do provide useful information on the…

利用灵活组播实验进行网络时延层析估计AI 解读

Summary Estimating and monitoring the quality of service of computer and communications networks is a problem of considerable interest. The paper focuses on estimating link level delay distributions from end-to-end path level data collected by using active probing experiments. This is an interesting large scale statistical inverse (deconvolution) problem. We describe a flexible class of probing experiments (‘flexicast’) for data collection and develop conditions under which…

马尔可夫调制泊松过程的精确Gibbs采样器AI 解读

Summary A Markov-modulated Poisson process is a Poisson process whose intensity varies according to a Markov process. We present a novel technique for simulating from the exact distribution of a continuous time Markov chain over an interval given the start and end states and the infinitesimal generator, and we use this to create a Gibbs sampler which samples from the exact distribution of the hidden Markov chain in a Markov-modulated Poisson process. We apply the Gibbs…

复 Bingham 四次分布与形状分析AI 解读

Summary The complex Bingham distribution was introduced by Kent as a tractable model for landmark-based shape analysis. It forms an exponential family with a sufficient statistic which is quadratic in the data. However, the distribution has too much symmetry to be widely useful. In particular, under high concentration it behaves asymptotically as a normal distribution, but where the covariance matrix is constrained to have complex symmetry. To overcome this limitation and to…

局部平稳过程的模型选择与半参数估计AI 解读

Summary Over recent decades increasingly more attention has been paid to the problem of how to fit a parametric model of time series with time-varying parameters. A typical example is given by autoregressive models with time-varying parameters. We propose a procedure to fit such time-varying models to general non-stationary processes. The estimator is a maximum Whittle likelihood estimator on sieves. The results do not assume that the observed process belongs to a specific…

用条件方法与协变量分解区分组间和组内效应AI 解读

Summary We consider the situation where the random effects in a generalized linear mixed model may be correlated with one of the predictors, which leads to inconsistent estimators. We show that conditional maximum likelihood can eliminate this bias. Conditional likelihood leads naturally to the partitioning of the covariate into between- and within-cluster components and models that include separate terms for these components also eliminate the source of the bias. Another…

非平稳自回归过程的相关图AI 解读

Summary Analysis of time series often involves correlograms and partial correlograms as graphical descriptions of temporal dependence. Two methods are available for computing these statistics: one based on autocorrelations and the other on scaled autocovariances. For a stationary time series the resulting plots are nearly identical. When it comes to time series exhibiting non-stationary features these methods can lead to very different results. This has two consequences:…

多元正态均值向量的最小体积置信域AI 解读

Summary Since Stein's original proposal in 1962, a series of papers have constructed confidence regions of smaller volume than the standard spheres for the mean vector of a multivariate normal distribution. A general approach to this problem is developed here and used to calculate a lower bound on the attainable volume. Bayes and fiducial methods are involved in the calculation. Scheffé-type problems are used to show that low volume by itself does not guarantee favourable…