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基于集合预报与贝叶斯模型平均的风速概率预测AI 解读

The current weather forecasting paradigm is deterministic, based on numerical models. Multiple estimates of the current state of the atmosphere are used to generate an ensemble of deterministic predictions. Ensemble forecasts, while providing information on forecast uncertainty, are often uncalibrated. Bayesian model averaging (BMA) is a statistical ensemble postprocessing method that creates calibrated predictive probability density functions (PDFs). Probabilistic wind…

你认识多少人？高效估计个人社会网络规模AI 解读

In this paper we develop a method to estimate both individual social network size (i.e., degree) and the distribution of network sizes in a population by asking respondents how many people they know in specific subpopulations (e.g., people named Michael). Building on the scale-up method of Killworth et al. (1998b) and other previous attempts to estimate individual network size, we propose a latent non-random mixing model which resolves three known problems with previous…

不可忽略无应答与抽样选择下小域 BMI 数据的贝叶斯分析AI 解读

Here we analyze body mass index (BMI) data for children and adolescents from the Third National Health and Nutrition Examination Survey (NHANES III). Because of the lack of BMI values for a considerable number of the children and adolescents, and the differential probabilities of selection of these individuals, serious nonresponse and selection bias in inference can be present. To analyze the NHANES III BMI data, a nonignorable nonresponse model has been proposed to estimate…

美国人口普查双系统准确性与覆盖评估中的局部后分层AI 解读

We consider a local post-stratification approach to analyze the capture–recapture dual system Accuracy and Coverage Evaluation (A.C.E.) data associated with the 2000 U.S. Census. The local post-stratification is carried out via a nonparametric regression estimation of the census enumeration and the correct enumeration functions. We propose a nonparametric population size estimator that is designed to accommodate some key aspects of the A.C.E.: missing values, erroneous…

大肠杆菌 SecYEG 孔道的单通道与多通道电流：含噪观测的模态数检验AI 解读

We analyze a new dataset from an electrophysiological recording of transmembrane currents through a bacterial membrane channel to demonstrate the existence of single and multiple channel currents. Protein channels mediate transport through biological membranes; knowledge of the channel properties gained from electrophysiological recordings is important for a targeted drug design. We investigate the bacterial membrane protein SecYEG which is of essential importance for the…

从水体折射图像正则化重建波高与坡度场AI 解读

Refractive imaging of wave fields in an established experimental technique. We consider the associated reconstruction problem and investigate some statistically motivated refinements, including (a) bias correction of local slope estimates, (b) regularization of directional slopes, (c) spatially weighted reconstruction using the estimated variability of local slope estimates, and (d) more accurate estimates of reference light profiles from time sequence data. These…

响应与协变量随机缺失的纵向数据加权广义估计函数AI 解读

Longitudinal studies often feature incomplete response and covariate data. It is well known that biases can arise from naive analyses of available data, but the precise impact of incomplete data depends on the frequency of missing data and the strength of the association between the response variables and covariates and the missing-data indicators. Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given…

医疗结局风险调整下的服务提供者评估：贝叶斯与频率学方法AI 解读

“Provider profiling” is the evaluation of the performance of hospitals, doctors, and other medical practitioners to enhance the quality of medical care. We propose a new method and compare conventional and Bayesian methodologies that are used or proposed for use for such “report cards.” Conventional statistical approaches to these provider assessments use likelihood-based frequentist methodologies, and the new Bayesian method is patterned after these. For each of three…

生存数据的高维变量选择AI 解读

The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a “ceiling effect” in which a tree simply cannot be grown deep enough to properly…

通过广义信息准则选择正则化参数AI 解读

We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity. In this paper, we propose employing the generalized information criterion (GIC), encompassing the commonly used Akaike information criterion (AIC) and Bayesian information criterion (BIC), for selecting the regularization parameter. Our proposal makes a…

河流网络空间统计模型的移动平均方法AI 解读

In this article we use moving averages to develop new classes of models in a flexible modeling framework for stream networks. Streams and rivers are among our most important resources, yet models with autocorrelated errors for spatially continuous stream networks have been described only recently. We develop models based on stream distance rather than on Euclidean distance. Spatial autocovariance models developed for Euclidean distance may not be valid when using stream…

变系数模型的局部秩推断AI 解读

By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible approach to modeling nonlinearity and interactions between covariates. This paper proposes a novel estimation procedure for the varying coefficient models based on local ranks. The new procedure provides a highly efficient and robust alternative to the local linear least squares method, and can be conveniently implemented using existing R…

多变量随机性的游程检验AI 解读

This paper proposes several extensions of the concept of runs to the multivariate setup, and studies the resulting tests of multivariate randomness against serial dependence. Two types of multivariate runs are defined: (i) an elliptical extension of the spherical runs proposed by Marden (1999), and (ii) an original concept of matrix-valued runs. The resulting runs tests themselves exist in various versions, one of which is a function of the number of data-based hyperplanes…

广义多层函数型回归AI 解读

We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models (GLMMs). Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well researched statistical framework. We propose and compare two methods for inference: 1) a two-stage frequentist…

已知参数强度形式下非齐次空间点过程二阶统计量的非参数方差估计AI 解读

We introduce new variance estimation procedures for second-order statistics that are computed from a single realization of intensity reweighted stationary spatial point processes. The statistics are defined either on a subset B of the observation window or on the whole window. For the former, we use subblocks that have the same size and shape as B as "replicates" of B in order to estimate the target variance. For the latter, we develop a subsampling estimator for a key…

匹配观察研究中敏感性分析的扩维解释AI 解读

A sensitivity analysis displays the increase in uncertainty that attends an inference when a key assumption is relaxed. In matched observational studies of treatment effects, a key assumption in some analyses is that subjects matched for observed covariates are comparable, and this assumption is relaxed by positing a relevant covariate that was not observed and not controlled by matching. What properties would such an unobserved covariate need to have to materially alter the…

分组假设的同时检验：利用外部分组信息AI 解读

In large-scale multiple testing problems, data are often collected from heterogeneous sources and hypotheses form into groups that exhibit different characteristics. Conventional approaches, including the pooled and separate analyses, fail to efficiently utilize the external grouping information. We develop a compound decision theoretic framework for testing grouped hypotheses and introduce an oracle procedure that minimizes the false nondiscovery rate subject to a…

具有参数模型特征的局部多项式分位数回归AI 解读

We propose a new approach to conditional quantile function estimation that combines both parametric and nonparametric techniques. At each design point, a global, possibly incorrect, pilot parametric model is locally adjusted through a kernel smoothing fit. The resulting quantile regression estimator behaves like a parametric estimator when the latter is correct and converges to the nonparametric solution as the parametric start deviates from the true underlying model. We…

向邻近链学习：并行链与区域自适应 MCMCAI 解读

Starting with the seminal paper of Haario, Saksman, and Tamminen (Haario, Saksman, and Tamminen 2001), a substantial amount of work has been done to validate adaptive Markov chain Monte Carlo algorithms. In this paper we focus on two practical aspects of adaptive Metropolis samplers. First, we draw attention to the deficient performance of standard adaptation when the target distribution is multimodal. We propose a parallel chain adaptation strategy that incorporates…

基于特征函数的经验似然方法及其 Lévy 过程应用AI 解读

Lévy processes have been receiving increasing attention in financial modeling. One distinctive feature of such models is that their characteristic functions are readily available. Inference based on characteristic functions is very useful for studying Lévy processes. By incorporating the recent advances in nonparametric approaches, empirical likelihood methods based on characteristic functions are developed in this paper for parameter estimation, testing a particular…

依赖条件下多重检验的因子模型方法AI 解读

The impact of dependence between individual test statistics is currently among the most discussed topics in the multiple testing of high-dimensional data literature, especially since Benjamini and Hochberg (1995) introduced the false discovery rate (FDR). Many papers have first focused on the impact of dependence on the control of the FDR. Some more recent works have investigated approaches that account for common information shared by all the variables to stabilize the…

结合变量选择的非参数贝叶斯条件分布建模AI 解读

This article considers a methodology for flexibly characterizing the relationship between a response and multiple predictors. Goals are (1) to estimate the conditional response distribution addressing the distributional changes across the predictor space, and (2) to identify important predictors for the response distribution change both within local regions and globally. We first introduce the probit stick-breaking process (PSBP) as a prior for an uncountable collection of…

多元正态混合模型的最大似然估计AI 解读

The Hessian of the multivariate normal mixture model is derived, and estimators of the information matrix are obtained, thus enabling consistent estimation of all parameters and their precisions. The usefulness of the new theory is illustrated with two examples and some simulation experiments. The newly proposed estimators appear to be superior to the existing ones.

泊松自回归AI 解读

In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the…