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Bayesian Spatio-Dynamic Modeling in Cell Motility Studies: Learning Nonlinear Taxic Fields Guiding the Immune Response

细胞运动的贝叶斯时空动态建模:学习引导免疫反应的非线性趋性场

Ioanna Manolopouloua, Melanie P. Matheub, Michael D. Cahalanb, Mike Westa & Thomas B. Keplerc a Department of Statistical Science , Duke University , Durham , NC , 27708 b Department of Physiology and Biophysics, School of Medicine , University of California , Irvine , CA , 92697 c Department of Microbiology , Boston University School of Medicine , Boston , MA , 02118 We develop and analyze models of the spatio-temporal organization of lymphocytes in the lymph nodes and…

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The Variance Profile

方差谱剖面

The variance profile is defined as the power mean of the spectral density function of a stationary stochastic process. It is a continuous and nondecreasing function of the power parameter, p, which returns the minimum of the spectrum (p→−∞), the interpolation error variance (harmonic mean, p=−1), the prediction error variance (geometric mean, p=0), the unconditional variance (arithmetic mean, p=1), and the maximum of the spectrum (p→∞). The variance profile provides a useful…

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Mixture of Regression Models With Varying Mixing Proportions: A Semiparametric Approach

混合比例可变的回归混合模型:半参数方法

In this article, we study a class of semiparametric mixtures of regression models, in which the regression functions are linear functions of the predictors, but the mixing proportions are smoothing functions of a covariate. We propose a one-step backfitting estimation procedure to achieve the optimal convergence rates for both regression parameters and the nonparametric functions of mixing proportions. We derive the asymptotic bias and variance of the one-step estimate, and…

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Conditional Inference Functions for Mixed-Effects Models With Unspecified Random-Effects Distribution

随机效应分布未指定时混合效应模型的条件推断函数

In longitudinal studies, mixed-effects models are important for addressing subject-specific effects. However, most existing approaches assume a normal distribution for the random effects, and this could affect the bias and efficiency of the fixed-effects estimator. Even in cases where the estimation of the fixed effects is robust with a misspecified distribution of the random effects, the estimation of the random effects could be invalid. We propose a new approach to…

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Does Marriage Boost Men’s Wages?: Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data

婚姻会提高男性工资吗?面板固定效应回归中的处理效应识别

Social scientists have generated a large and inconclusive literature on the effect(s) of marriage on men’s wages. Researchers have hypothesized that the wage premium enjoyed by married men may reflect both a tendency for more productive men to marry and an effect of marriage on productivity. To sort out these explanations, researchers have used fixed effects regression models for panel data to adjust for selection on unobserved time-invariant confounders, interpreting…

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A Studentized Permutation Test for the Comparison of Spatial Point Patterns

比较空间点模式的学生化置换检验

In this study, a new test is proposed for the hypothesis that two (or more) observed point patterns are realizations of the same spatial point process model. To this end, the point patterns are divided into disjoint quadrats, on each of which an estimate of Ripley’s K-function is calculated. The two groups of empirical K-functions are compared by a permutation test using a Studentized test statistic. The proposed test performs convincingly in terms of empirical level and…

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DD -Classifier: Nonparametric Classification Procedure Based on DD -Plot

DD 分类器:基于深度对深度图的非参数分类方法

Using the DD-plot (depth vs. depth plot), we introduce a new nonparametric classification algorithm and call it DD-classifier. The algorithm is completely nonparametric, and it requires no prior knowledge of the underlying distributions or the form of the separating curve. Thus, it can be applied to a wide range of classification problems. The algorithm is completely data driven and its classification outcome can be easily visualized in a two-dimensional plot regardless of…

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Inverse Realized Laplace Transforms for Nonparametric Volatility Density Estimation in Jump-Diffusions

跳跃扩散中以逆已实现拉普拉斯变换估计波动率密度

This article develops a nonparametric estimator of the stochastic volatility density of a discretely observed Itô semimartingale in the setting of an increasing time span and finer mesh of the observation grid. There are two basic steps involved. The first step is aggregating the high-frequency increments into the realized Laplace transform, which is a robust nonparametric estimate of the underlying volatility Laplace transform. The second step is using a regularized kernel…

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Vast Portfolio Selection With Gross-Exposure Constraints

总敞口约束下的大规模投资组合选择

We introduce the large portfolio selection using gross-exposure constraints. We show that with gross-exposure constraint the empirically selected optimal portfolios based on estimated covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results in Jagannathan and Ma (2003). We also show that the no-short-sale…

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Quantile Periodograms

分位数周期图

Two periodogram-like functions, called quantile periodograms, are introduced for spectral analysis of time series. The quantile periodograms are constructed from trigonometric quantile regression and motivated by different interpretations of the ordinary periodogram. Analytical and numerical results demonstrate the capability of the quantile periodograms for detecting hidden periodicity in the quantiles and for providing an additional view of time-series data. A connection…

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Structured, Sparse Aggregation

结构化稀疏聚合

This article introduces a method for aggregating many least-squares estimators so that the resulting estimate has two properties: sparsity and structure. That is, only a few candidate covariates are used in the resulting model, and the selected covariates follow some structure over the candidate covariates that is assumed to be known a priori. Although sparsity is well studied in many settings, including aggregation, structured sparse methods are still emerging. We…

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Probabilistic Foundation of Confirmatory Adaptive Designs

验证性自适应设计的概率基础

Adaptive designs allow the investigator of a confirmatory trial to react to unforeseen developments by changing the design. This broad flexibility comes at the price of a complex statistical model where important components, such as the adaptation rule, remain unspecified. It has thus been doubted whether Type I error control can be guaranteed in general adaptive designs. This criticism is fully justified as long as the probabilistic framework on which an adaptive design is…

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Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists

配对排名列表随机退化的中等偏差推断

Consider a problem where N items (objects or individuals) are judged by assessors using their perceptions of a set of performance criteria, or alternatively by technical devices. In particular, two assessors might rank the items between 1 and N on the basis of relative performance, independently of each other. We can aggregate the rank lists by assigning one if the two assessors agree, and zero otherwise, and we can modify this approach to make it robust against…

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A Martingale Representation for Matching Estimators

匹配估计量的鞅表示

Matching estimators are widely used in statistical data analysis. However, the large sample distribution of matching estimators has been derived only for particular cases. This article establishes a martingale representation for matching estimators. This representation allows the use of martingale limit theorems to derive the large sample distribution of matching estimators. As an illustration of the applicability of the theory, we derive the asymptotic distribution of a…

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Informative Estimation and Selection of Correlation Structure for Longitudinal Data

纵向数据相关结构的信息性估计与选择

Identifying an informative correlation structure is important in improving estimation efficiency for longitudinal data. We approximate the empirical estimator of the correlation matrix by groups of known basis matrices that represent different correlation structures, and transform the correlation structure selection problem to a covariate selection problem. To address both the complexity and the informativeness of the correlation matrix, we minimize an objective function…

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Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event

信息性观测时点与依赖终止事件下的纵向数据联合分析

In many longitudinal studies, repeated measures are often correlated with observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint model for the analysis of longitudinal data in the presence of both informative observation times and a dependent terminal event via latent variables. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are…

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The Geometry of Nonparametric Filament Estimation

非参数丝状结构估计的几何学

We consider the problem of estimating filamentary structure from d-dimensional point process data. We make some connections with computational geometry and develop nonparametric methods for estimating the filaments. We show that, under weak conditions, the filaments have a simple geometric representation as the medial axis of the data distribution’s support. Our methods convert an estimator of the support’s boundary into an estimator of the filaments. We also find the rates…

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Smooth Blockwise Iterative Thresholding: A Smooth Fixed Point Estimator Based on the Likelihood’s Block Gradient

平滑分块迭代阈值法:基于似然分块梯度的固定点估计

The proposed smooth blockwise iterative thresholding estimator (SBITE) is a model selection technique defined as a fixed point reached by iterating a likelihood gradient-based thresholding function. The smooth James–Stein thresholding function has two regularization parameters λ and ν, and a smoothness parameter s. It enjoys smoothness like ridge regression and selects variables like lasso. Focusing on Gaussian regression, we show that SBITE is uniquely defined, and that its…

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Multiple Testing of Composite Null Hypotheses in Heteroscedastic Models

异方差模型中复合原假设的多重检验

In large-scale studies, the true effect sizes often range continuously from zero to small to large, and are observed with heteroscedastic errors. In practical situations where the failure to reject small deviations from the null is inconsequential, specifying an indifference region (or forming composite null hypotheses) can greatly reduce the number of unimportant discoveries in multiple testing. The heteroscedasticity issue poses new challenges for multiple testing with…

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Meta-Analysis of Rare Binary Adverse Event Data

罕见二元不良事件数据的元分析

We examine the use of fixed-effects and random-effects moment-based meta-analytic methods for analysis of binary adverse event data. Special attention is paid to the case of rare adverse events which are commonly encountered in routine practice. We study estimation of model parameters and between-study heterogeneity. In addition, we examine traditional approaches to hypothesis testing of the average treatment effect and detection of the heterogeneity of treatment effect…

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Evaluating the Effect of Early Versus Late ARV Regimen Change if Failure on an Initial Regimen: Results From the AIDS Clinical Trials Group Study A5095

初始抗逆转录病毒方案失败后早期与延迟换药的效果评估

The current goal of initial antiretroviral (ARV) therapy is suppression of plasma human immunodeficiency virus (HIV)-1 RNA levels to below 200 copies per milliliter. A proportion of HIV-infected patients who initiate antiretroviral therapy in clinical practice or antiretroviral clinical trials either fail to suppress HIV-1 RNA or have HIV-1 RNA levels rebound on therapy. Frequently, these patients have sustained CD4 cell counts responses and limited or no clinical symptoms…

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Nonparametric Bayesian Multiple Imputation for Missing Data Due to Mid-Study Switching of Measurement Methods

研究中途更换测量方法所致缺失数据的非参数贝叶斯多重插补

Investigators often change how variables are measured during the middle of data-collection, for example, in hopes of obtaining greater accuracy or reducing costs. The resulting data comprise sets of observations measured on two (or more) different scales, which complicates interpretation and can create bias in analyses that rely directly on the differentially measured variables. We develop approaches based on multiple imputation for handling mid-study changes in measurement…

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Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer

晚期前列腺癌序贯随机试验中可实施动态治疗策略的评估

We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients were to be initially randomized among four chemotherapies, and that those who responded poorly were to be rerandomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs' overall success rates, with success defined by the occurrence of…

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New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing

时间序列拟合优度检验的新加权混成统计量

We exploit ideas from high-dimensional data analysis to derive new portmanteau tests that are based on the trace of the square of the mth order autocorrelation matrix. The resulting statistics are weighted sums of the squares of the sample autocorrelation coefficients that, unlike many other tests appearing in the literature, are numerically stable even when the number of lags considered is relatively close to the sample size. The statistics behave asymptotically as a linear…

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