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利用全变差惩罚估计多个变点AI 解读

We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise. Our approach consists in reframing this task in a variable selection context. We use a penalized least-square criterion with a ℓ1-type penalty for this purpose. We explain how to implement this method in practice by using the LARS / LASSO algorithm. We then prove that, in an appropriate asymptotic framework, this…

识别阵列 CGH 数据共享拷贝数异常的贝叶斯随机分段模型AI 解读

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy…

通过数据克隆研究广义线性混合模型的可估性与似然推断AI 解读

Maximum likelihood estimation for Generalized Linear Mixed Models (GLMM), an important class of statistical models with substantial applications in epidemiology, medical statistics, and many other fields, poses significant computational difficulties. In this article, we use data cloning, a simple computational method that exploits advances in Bayesian computation, in particular the Markov Chain Monte Carlo method, to obtain maximum likelihood estimators of the parameters in…

高维自适应非线性交互结构的变量选择AI 解读

Numerous penalization based methods have been proposed for fitting a traditional linear regression model in which the number of predictors, p, is large relative to the number of observations, n. Most of these approaches assume sparsity in the underlying coefficients and perform some form of variable selection. Recently, some of this work has been extended to nonlinear additive regression models. However, in many contexts one wishes to allow for the possibility of…

相关数据边际广义可加模型的一致模型选择AI 解读

We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified likelihood function for the generalized additive model, because the outcomes could be nonnormal and discrete, which makes…

受约束因子模型AI 解读

This article considers estimation and applications of constrained and partially constrained factor models when the dimension of explanatory variables is high. Both the classical and approximate factor models are investigated. For estimation, we employ both the maximum likelihood and least squares methods. We show that the least squares estimation is based on constrained principal component analysis and provides consistent estimates for the model under certain conditions. The…

动态空间泊松过程的自回归混合模型：暴力犯罪强度追踪AI 解读

This article develops a set of tools for smoothing and prediction with dependent point event patterns. The methodology is motivated by the problem of tracking weekly maps of violent crime events, but is designed to be straightforward to adapt to a wide variety of alternative settings. In particular, a Bayesian semiparametric framework is introduced for modeling correlated time series of marked spatial Poisson processes. The likelihood is factored into two independent…

利用变化的响应倾向校正调查无响应AI 解读

All surveys with less than full response potentially suffer from nonresponse bias. Poststratification weights can only correct for selection into the sample based on observables whose distribution is known in the population. Variables such as gender, race, income, and region satisfy this requirement because they are available from the U.S. Census Bureau, but poststratification based on these variables may not eliminate nonresponse bias. I develop an approach for correcting…

含噪声且异步金融数据的高频协方差估计AI 解读

This article proposes a consistent and efficient estimator of the high-frequency covariance (quadratic covariation) of two arbitrary assets, observed asynchronously with market microstructure noise. This estimator is built on the marriage of the quasi–maximum likelihood estimator of the quadratic variation and the proposed generalized synchronization scheme and thus is not influenced by the Epps effect. Moreover, the estimation procedure is free of tuning parameters or…

利用风险人口的空间汇总信息估计个体风险AI 解读

We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. We develop data-driven methods to select the weights…

通过累积切片估计实现回归充分降维AI 解读

In this paper we offer a complete methodology of cumulative slicing estimation to sufficient dimension reduction. In parallel to the classical slicing estimation, we develop three methods that are termed, respectively, as cumulative mean estimation, cumulative variance estimation, and cumulative directional regression. The strong consistency for p=O(n1 / 2 / log n) and the asymptotic normality for p=o(n1 / 2) are established, where p is the dimension of the predictors and n…

在早产儿围产期护理观察研究中构建更强工具变量AI 解读

An instrument is a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment. Nonetheless, in settings in which treatment assignment is mostly deliberate and not random, there may exist some essentially random nudges to accept treatment, so that use of an instrument might extract bits of random treatment assignment from a setting that is otherwise quite biased in its treatment assignments. An instrument…

不完整纵向二元数据的贝叶斯收缩模型AI 解读

We consider inference in randomized longitudinal studies with missing data that is generated by skipped clinic visits and loss to follow-up. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and partial ignorability for the intermittent missingness. We posit an exponential tilt model that links non-identifiable distributions and…

已实现波动率的局部建模AI 解读

With the recent availability of high-frequency financial data the long-range dependence of volatility regained researchers’ interest and has led to the consideration of long-memory models for volatility. The long-range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory…

利用成对 Copula 分解序列依赖的纵向数据建模AI 解读

Copulas have proven to be very successful tools for the flexible modeling of cross-sectional dependence. In this paper we express the dependence structure of continuous-valued time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a “vine” in the graphical models literature, where each copula is entitled a “pair-copula.” We propose a Bayesian approach for the estimation of this dependence structure for longitudinal…

竞争传染病原体的贝叶斯建模：实验室结果不完整的流感研究AI 解读

In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often co-circulate with influenza and cause influenza-like illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among…

合成后抽样：发布人口普查公共使用微观数据的新方法AI 解读

Many statistical agencies disseminate samples of census microdata, that is, data on individual records, to the public. Before releasing the microdata, agencies typically alter identifying or sensitive values to protect data subjects’ confidentiality, for example by coarsening, perturbing, or swapping data. These standard disclosure limitation techniques distort relationships and distributional features in the original data, especially when applied with high intensity.…

利用简短筛查量表估计学校层面的精神疾病患病率AI 解读

We use data collected in the National Comorbidity Survey - Adolescent (NCS-A) to develop a methodology to estimate the small-area prevalence of serious emotional distress (SED) in schools in the United States, exploiting the clustering of the main NCS-A sample by school. The NCS-A instrument includes both a short screening scale, the K6, and extensive diagnostic assessments of the individual disorders and associated impairment that determine the diagnosis of SED. We fitted a…

企业撤回 IPO 决策的自选择：带反馈的破产风险模型贝叶斯推断AI 解读

Examination on firm performance subsequent to a chosen event is widely used in finance studies to analyze the motivation behind managerial decisions. However, results are often subject to bias when the self-selectivity behind managerial decisions is ignored and unspecified. This study investigates a unique corporate event of initial public offering (IPO) withdrawal, where a firm’s subsequent likelihood of bankruptcy is specified in a system of switching hazard models, and…

评论：古气候重建的层级统计建模AI 解读

The article by Bo Li, Douglas W. Nychka, and Caspar M. Ammann (hereafter, LNA) has several goals. It considers the important problem of reconstruction of past (over a period of more than 1000 Years Before Present) climate from multiproxy data, and it directly recognizes the various uncertainties in this undertaking. These uncertainties are expressed through (conditional) probability distributions in a framework known to readers of this journal as hierarchical statistical…

多代理指标重建历史气候的价值AI 解读

Understanding the dynamics of climate change in its full richness requires the knowledge of long temperature time series. Although long-term, widely distributed temperature observations are not available, there are other forms of data, known as climate proxies, that can have a statistical relationship with temperatures and have been used to infer temperatures in the past before direct measurements. We propose a Bayesian hierarchical model to reconstruct past temperatures…

线性混合效应模型的最优划分：识别安慰剂响应者AI 解读

A long-standing problem in clinical research is distinguishing drug treated subjects that respond due to specific effects of the drug from those that respond to non-specific (or placebo) effects of the treatment. Linear mixed effect models are commonly used to model longitudinal clinical trial data. In this paper we present a solution to the problem of identifying placebo responders using an optimal partitioning methodology for linear mixed effects models. Since individual…

近似贝叶斯计算的非参数视角AI 解读

Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics sobs from the data and simulating summary statistics for different values of the parameter Θ. The posterior distribution is then approximated by an estimator of the conditional density g(Θ|sobs). In this paper, we…

稀疏信号段的最优识别及其拷贝数变异应用AI 解读

Motivated by DNA copy number variation (CNV) analysis based on high-density single nucleotide polymorphism (SNP) data, we consider the problem of detecting and identifying sparse short segments in a long one-dimensional sequence of data with additive Gaussian white noise, where the number, length and location of the segments are unknown. We present a statistical characterization of the identifiable region of a segment where it is possible to reliably separate the segment…