论文检索

利用非平稳隐马尔可夫模型预测蛋白质结构域间连接区AI 解读

Proteins have two types of regions: linker regions that connect active domains and nonlinker regions, which include domains and terminal residues. It is important to try to distinguish these regions using information in the sequence of amino acids that, when folded, form the protein. In this article we develop a nonstationary hidden Markov model to infer region boundaries. The approach is based on a latent variable defined on the sequence; this variable is a continuous index…

风险厌恶者是否更乐观？一种贝叶斯估计方法AI 解读

Abstract Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance. Its estimation leads to a non‐trivial statistical problem. We start from a large lottery survey (1536 individuals). We assume that individuals have true unobservable characteristics. We adopt a Bayesian approach and use a hybrid MCMC approximation method to numerically estimate the…

BMI 与工资：偏态处理—响应模型及非参数内生性的贝叶斯分析AI 解读

Abstract We generalize the specifications used in previous studies of the effect of body mass index (BMI) on earnings by allowing the potentially endogenous BMI variable to enter the log wage equation nonparametrically. We introduce a Bayesian posterior simulator for fitting our model that permits a nonparametric treatment of the endogenous BMI variable, flexibly accommodates skew in the BMI distribution, and whose implementation requires only Gibbs steps. Using data from…

含不可观测异质性的动态 Tobit 面板模型：半参数贝叶斯推断AI 解读

Abstract This paper develops semiparametric Bayesian methods for inference of dynamic Tobit panel data models. Our approach requires that the conditional mean dependence of the unobserved heterogeneity on the initial conditions and the strictly exogenous variables be specified. Important quantities of economic interest such as the average partial effect and average transition probabilities can be readily obtained as a by‐product of the Markov chain Monte Carlo run. We apply…

后验剖面分布的一般频率学派性质AI 解读

In this paper, inference for the parametric component of a semiparametric model based on sampling from the posterior profile distribution is thoroughly investigated from the frequentist viewpoint. The higher-order validity of the profile sampler obtained in Cheng and Kosorok [Ann. Statist. 36 (2008)] is extended to semiparametric models in which the infinite dimensional nuisance parameter may not have a root-n convergence rate. This is a nontrivial extension because it…

在迷宫中搜寻证据路径AI 解读

Consider a graph with a set of vertices and oriented edges connecting pairs of vertices. Each vertex is associated with a random variable and these are assumed to be independent. In this setting, suppose we wish to solve the following hypothesis testing problem: under the null, the random variables have common distribution N(0, 1) while under the alternative, there is an unknown path along which random variables have distribution N(μ, 1), μ> 0, and distribution N(0, 1) away…

利用剖面采样器进行高阶半参数频率学推断AI 解读

We consider higher order frequentist inference for the parametric component of a semiparametric model based on sampling from the posterior profile distribution. The first order validity of this procedure established by Lee, Kosorok and Fine in [J. American Statist. Assoc. 100 (2005) 960–969] is extended to second-order validity in the setting where the infinite-dimensional nuisance parameter achieves the parametric rate. Specifically, we obtain higher order estimates of the…

学习、预测与结构突变AI 解读

Abstract We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and model comparison are used to derive a predictive density that takes into account the possibility that a break will occur before the next observation. Estimates for the posterior distribution of the most recent break are generated as a by‐product of our procedure. We discuss the importance of…

通过幂后验估计边际似然AI 解读

Summary Model choice plays an increasingly important role in statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. However, this is typically a difficult task since it amounts to integrating over all model parameters. The aim of the paper is to illustrate how this may be achieved by using ideas from thermodynamic integration or path sampling. We show how the marginal likelihood can be computed via Markov…

基于小波或提升格式的缺失值插补方法AI 解读

Summary The paper proposes a new approach to imputation using the expected sparse representation of a surface in a wavelet or lifting scheme basis. Our method incorporates a Bayesian mixture prior for these wavelet coefficients into a Gibbs sampler to generate a complete posterior distribution for the variable of interest. Intuitively, the estimator operates by borrowing strength from those observed neighbouring values to impute at the unobserved sites. We demonstrate the…

由二元性状数据推断带年代的祖先树及其语言分化应用AI 解读

Summary Binary trait data record the presence or absence of distinguishing traits in individuals. We treat the problem of estimating ancestral trees with time depth from binary trait data. Simple analysis of such data is problematic. Each homology class of traits has a unique birth event on the tree, and the birth event of a trait that is visible at the leaves is biased towards the leaves. We propose a model-based analysis of such data and present a Markov chain Monte Carlo…

评论：博弈中偏好与信念参数的识别AI 解读

The paper by Andres Aradillas-Lopez and Elie Tamer clari…es how identi…cation can be obtained in games and provides techniques to do so.In other words, it o¤ers constructive ways of analyzing either point or set identi…cation of parameters governing the preferences and beliefs of players in di¤erent games.In this short comment, I am trying to highlight the key issues that are brought out by the paper.First, as sometimes alluded to by the authors, one way of reading their…

异方差测量误差下的密度估计AI 解读

We consider density estimation when the variable of interest is subject to heteroscedastic measurement error. The density is assumed to have a smooth but unknown functional form that we model with a penalized mixture of B-splines. We treat the situation in which multiple mismeasured observations of each variable of interest are observed for at least some of the subjects, and the measurement error is assumed to be additive and normal. The measurement error variance function…

含多个协变量的大规模函数数据惩罚聚类AI 解读

In this article we propose a penalized clustering method for large-scale data with multiple covariates through a functional data approach. In our proposed method, responses and covariates are linked together through nonparametric multivariate functions (fixed effects), which have great flexibility in modeling various function features, such as jump points, branching, and periodicity. Functional ANOVA is used to further decompose multivariate functions in a reproducing kernel…

存在依赖性终止时复发事件数据的现有分析方法AI 解读

There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid the future users in understanding the implications of modeling assumptions and modeling properties, we review the state of the art statistical methods and present novel theoretical properties, identifiability results and practical consequences of key modeling assumptions of several fully…

从实验数据学习因果贝叶斯网络结构AI 解读

We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space Markov chain Monte Carlo, equi-energy sampling, importance weighting, and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures.

阵列比较基因组杂交数据的贝叶斯隐马尔可夫建模AI 解读

Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic…

利用卷积模型对质子加速器进行统计推断AI 解读

Proton beams present difficulties in analysis because of the limited data that can be collected. The study of such beams must depend on complex computer simulators that incorporate detailed physical equations. The statistical problem of interest is to infer the initial state of the beam from the limited data collected as the beam passes through a series of focusing magnets. We are thus faced with a classic inverse problem where the computer simulator links the initial state…

功能相关基因中多态性的贝叶斯选择与聚类AI 解读

In epidemiologic studies, there is often interest in assessing the relationship between polymorphisms in functionally related genes and a health outcome. For each candidate gene, single nucleotide polymorphism (SNP) data are collected at a number of locations, resulting in a large number of possible genotypes. Because instabilities can result in analyses that include all the SNPs, dimensionality is typically reduced by conducting single SNP analyses or attempting to identify…

装袋法对经济时间序列预测有多大帮助？美国消费价格通胀案例AI 解读

This article focuses on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction mean squared error of forecasting models of U.S. consumer price inflation. We propose three variants of the bagging algorithm specifically designed for this type of forecasting problem and evaluate their empirical performance. Although bagging predictors in our application are clearly more accurate than equally weighted forecasts, median…

半参数过度离散广义线性模型中的变量选择与模型平均AI 解读

We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model and whether they enter linearly or flexibly. When the variance term is null, we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation, and the…

利用高维输出校准计算机模型AI 解读

This work focuses on combining observations from field experiments with detailed computer simulations of a physical process to carry out statistical inference. Of particular interest here is determining uncertainty in resulting predictions. This typically involves calibration of parameters in the computer simulator as well as accounting for inadequate physics in the simulator. The problem is complicated by the fact that simulation code is sufficiently demanding that only a…

预测密度估计的容许性AI 解读

Let X|μ∼Np(μ, vxI) and Y|μ∼Np(μ, vyI) be independent p-dimensional multivariate normal vectors with common unknown mean μ. Based on observing X=x, we consider the problem of estimating the true predictive density p(y|μ) of Y under expected Kullback–Leibler loss. Our focus here is the characterization of admissible procedures for this problem. We show that the class of all generalized Bayes rules is a complete class, and that the easily interpretable conditions of Brown and…

基于高斯过程先验的后验分布收缩速率AI 解读

We derive rates of contraction of posterior distributions on nonparametric or semiparametric models based on Gaussian processes. The rate of contraction is shown to depend on the position of the true parameter relative to the reproducing kernel Hilbert space of the Gaussian process and the small ball probabilities of the Gaussian process. We determine these quantities for a range of examples of Gaussian priors and in several statistical settings. For instance, we consider…