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Generalized Fiducial Inference: A Review and New Results

广义信赖推断:综述与新结果

R. A. Fisher, the father of modern statistics, proposed the idea of fiducial inference during the first half of the 20th century. While his proposal led to interesting methods for quantifying uncertainty, other prominent statisticians of the time did not accept Fisher’s approach as it became apparent that some of Fisher’s bold claims about the properties of fiducial distribution did not hold up for multi-parameter problems. Beginning around the year 2000, the authors and…

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Detecting and Localizing Differences in Functional Time Series Dynamics: A Case Study in Molecular Biophysics

函数型时间序列动态差异的检测与定位:分子生物物理案例

Motivated by the problem of inferring the molecular dynamics of DNA in solution, and linking them with its base-pair composition, we consider the problem of comparing the dynamics of functional time series (FTS), and of localizing any inferred differences in frequency and along curvelength. The approach we take is one of Fourier analysis, where the complete second-order structure of the FTS is encoded by its spectral density operator, indexed by frequency and curvelength.…

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Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

用于基因网络推断的两阶段自适应 Lasso 有向无环图估计

Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed graphical models, where all the edges are directed edges and contain no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high…

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Template-Based Models for Genome-Wide Analysis of Next-Generation Sequencing Data at Base-Pair Resolution

基于模板的碱基对分辨率全基因组测序分析模型

We consider the problem of estimating the genome-wide distribution of nucleosome positions from paired-end sequencing data. We develop a modeling approach based on nonparametric templates to control for the variability along the sequence of read counts associated with nucleosomal DNA due to enzymatic digestion and other sample preparation steps, and we develop a calibrated Bayesian method to detect local concentrations of nucleosome positions. We also introduce a set of…

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How Much Information Does Dependence Between Wavelet Coefficients Contain?

小波系数之间的依赖包含多少信息?

This article is motivated by several articles that propose statistical inference where the independence of wavelet coefficients for both short- as well as long-range dependent time series is assumed. We focus on the sample variance and investigate the influence of the dependence between wavelet coefficients and this statistic. To this end, we derive asymptotic distributional properties of the sample variance for a time series that is synthesized, ignoring some or all…

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Graphical Procedures for Multiple Comparisons Under General Dependence

一般依赖下多重比较的图形化程序

It has been more than half a century since Tukey first introduced graphical displays that relate nonoverlap of confidence intervals to statistically significant differences between parameter estimates. In this article, we show how Tukey’s graphical overlap procedure can be modified to accommodate general forms of dependence within and across samples. We also develop a procedure that can be used to more effectively resolve rankings within the tails of the distributions of…

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Joint Inference for Competing Risks Survival Data

竞争风险生存数据的联合推断

This article develops joint inferential methods for the cause-specific hazard function and the cumulative incidence function of a specific type of failure to assess the effects of a variable on the time to the type of failure of interest in the presence of competing risks. Joint inference for the two functions are needed in practice because (i) they describe different characteristics of a given type of failure, (ii) they do not uniquely determine each other, and (iii) the…

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Stabilized Nearest Neighbor Classifier and its Statistical Properties

稳定化最近邻分类器及其统计性质

The stability of statistical analysis is an important indicator for reproducibility, which is one main principle of the scientific method. It entails that similar statistical conclusions can be reached based on independent samples from the same underlying population. In this article, we introduce a general measure of classification instability (CIS) to quantify the sampling variability of the prediction made by a classification method. Interestingly, the asymptotic CIS of…

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Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times

带序贯转移时间的动态治疗方案的贝叶斯非参数估计

We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially…

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Coverage Inducing Priors in Nonstandard Inference Problems

非标准推断问题中诱导频率覆盖率的先验

Ulrich K. Müllera & Andriy Noretsb*a Department of Economics, Princeton University, Princeton, NJ, USAb Economics Department, Brown University, Providence, RI, USACONTACT Andriy Norets andriy_norets@brown.edu Economics Department, Brown University, Providence, RI 02912Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTWe consider the construction of set estimators that possess both Bayesian credibility and…

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A Subsampled Double Bootstrap for Massive Data

面向海量数据的子抽样双重自助法

The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets that are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently, Kleiner and co-authors proposed a method called BLB (bag of little bootstraps) for massive data, which is more computationally scalable with little…

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Bayesian Nonparametric Longitudinal Data Analysis

贝叶斯非参数纵向数据分析

Fernando A. Quintanaa*, Wesley O. Johnsonb, L. Elaine Waetjenc & Ellen B. Goldca Departamento de Estadística, Pontificia Universidad Católica de Chile Santiago, Chileb Department of Statistics, University of California Irvine, Irvine, CA, USAc Department of Obstetrics and Gynecology, and Department of Public Health Sciences, Division of Epidemiology, University of California Davis, Davis, CA, USACONTACT Fernando A. Quintana quintana@mat.uc.cl Departamento de Estadística,…

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Fast Estimation of Regression Parameters in a Broken-Stick Model for Longitudinal Data

纵向数据折线回归模型参数的快速估计

Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this article we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods…

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Generalized Dynamic Principal Components

广义动态主成分

Brillinger defined dynamic principal components (DPC) for time series based on a reconstruction criterion. He gave a very elegant theoretical solution and proposed an estimator which is consistent under stationarity. Here, we propose a new enterally empirical approach to DPC. The main differences with the existing methods—mainly Brillinger procedure—are (1) the DPC we propose need not be a linear combination of the observations and (2) it can be based on a variety of loss…

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Rejoinder: In-Sample Inference and Forecasting in Misspecified Factor Models

答复:误设因子模型中的样本内推断与预测

We thank all the discussants for their constructive comments and stimulating discussions of our article. Each of them raised interesting and insightful issues on various themes, which we discuss in...

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In-Sample Inference and Forecasting in Misspecified Factor Models

误设因子模型中的样本内推断与预测

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning…

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Using the Bootstrap to Test for Symmetry Under Unknown Dependence

未知依赖结构下利用自助法检验对称性

This article considers tests for symmetry of the one-dimensional marginal distribution of fractionally integrated processes. The tests are implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the relevant test statistics. The sieve bootstrap allows inference on symmetry to be carried out without knowledge of either the memory parameter of the data or of the appropriate norming factor for the test statistic and its…

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Measuring Social Tension from Income Class Segregation

基于收入阶层隔离的社会张力度量

We develop an index that effectively measures the level of social tension generated by income class segregation. We adopt the basic concepts of between-group difference (or alienation) and within-group similarity (or identification) from the income [bi]polarization literature; but we allow for asymmetric degrees of between-group antagonism in the alienation function, and construct a more effective identification function using both the relative degree of within-group…

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Testing Hypotheses in Nonparametric Models of Production

非参数生产模型的假设检验

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units,…

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Maximum-Entropy Prior Uncertainty and Correlation of Statistical Economic Data

经济统计数据的最大熵先验不确定性与相关性

Empirical estimates of source statistical economic data such as trade flows, greenhouse gas emissions, or employment figures are always subject to uncertainty (stemming from measurement errors or confidentiality) but information concerning that uncertainty is often missing. This article uses concepts from Bayesian inference and the maximum entropy principle to estimate the prior probability distribution, uncertainty, and correlations of source data when such information is…

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Model averaging in semiparametric estimation of treatment effects

处理效应半参数估计中的模型平均

Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the ATT. The proposed procedures minimize the estimated MSE of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision…

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The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series

交叉分位相关图:分位依赖与方向预测性检验

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross-quantilogram and the corresponding test statistic. The limiting distributions depend on nuisance parameters. To construct consistent confidence intervals we employ a stationary bootstrap procedure; we…

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Goodness-of-fit test for specification of semiparametric copula dependence models

半参数 Copula 依赖模型的拟合优度检验

This paper concerns goodness-of-fit tests for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between “in-sample” and “out-of-sample” pseudo-likelihoods. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space. We establish the asymptotic normality and investigate…

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A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous

持续且内生的微观结构效应与采样时长下证券协变矩阵的偏差校正

I propose a bias-corrected non-parametric estimator of the covariation matrix of log security prices, designed as a convex combination of two realized kernels. The estimator is simple but possesses desirable statistical properties including consistency, asymptotic normality and the parametric rate of convergence in the presence of persistent, diurnally heteroskedastic and endogenous microstructure effects. It is robust to the asynchronous trading of multiple securities with…

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