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Censored quantile regression with time-dependent covariates

含时变协变量的删失分位数回归

Abstract We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be…

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Semiparametric modeling and analysis for longitudinal network data

纵向网络数据的半参数建模与分析

We introduce a semiparametric latent space model for analyzing longitudinal network data. The model consists of a static latent space component and a time-varying node-specific baseline component. We develop a semiparametric efficient score equation for the latent space parameter by adjusting for the baseline nuisance component. Estimation is accomplished through a one-step update estimator and an appropriately penalized maximum likelihood estimator. We derive oracle error…

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Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng

何银秋、顾玉琪、应志良对《使用 Varimax 的经典因子分析可进行统计推断》的讨论

We congratulate the authors on an impressive paper that demystifies the popular Varimax rotation. They showed that the Varimax rotation can identify true axes of latent factors under the leptokurtic condition in a low-rank semiparametric factor model Different from (1), the exploratory Item Factor Analysis (IFA), widely used in social and behavioural sciences, adopts a nonlinear transformation of the low-rank structure: where the binary data matrix R contains N individuals’…

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Regression Analysis of Doubly Truncated Data

双重截断数据的回归分析

Doubly truncated data are found in astronomy, econometrics and survival analysis literature. They arise when each observation is confined to an interval, i.e., only those which fall within their respective intervals are observed along with the intervals. Unlike the one-sided truncation that can be handled by counting process-based approach, doubly truncated data are much more difficult to handle. In their analysis of an astronomical data set, Efron and Petrosian (1999)…

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Chernoff index for Cox test of separate parametric families

分离参数族 Cox 检验的 Chernoff 指数

The asymptotic efficiency of a generalized likelihood ratio test proposed by Cox is studied under the large deviations framework for error probabilities developed by Chernoff. In particular, two separate parametric families of hypotheses are considered (Cox, 1961, 1962). The significance level is set such that the maximal type I and type II error probabilities for the generalized likelihood ratio test decay exponentially fast with the same rate. We derive the analytic form…

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Statistical Analysis of Q -Matrix Based Diagnostic Classification Models

基于 Q 矩阵的诊断分类模型统计分析

Diagnostic classification models (DMCs) have recently gained prominence in educational assessment, psychiatric evaluation, and many other disciplines. Central to the model specification is the so-called Q-matrix that provides a qualitative specification of the item-attribute relationship. In this article, we develop theories on the identifiability for the Q-matrix under the DINA and the DINO models. We further propose an estimation procedure for the Q-matrix through the…

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Semiparametric Transformation Models for Causal Inference in Time-to-Event Studies with All-or-Nothing Compliance

完全依从或完全不依从生存研究中的因果推断:半参数变换模型

Summary We consider causal inference in randomized survival studies with right-censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditionally on covariates and latent compliance type. Estimands depending on these distributions, e.g. the complier average causal effect, the complier effect on survival beyond time t and the complier quantile effect, are…

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Oracle inequalities for the lasso in the Cox model

Cox模型中Lasso的预言机不等式

We study the absolute penalized maximum partial likelihood estimator in sparse, high-dimensional Cox proportional hazards regression models where the number of time-dependent covariates can be larger than the sample size. We establish oracle inequalities based on natural extensions of the compatibility and cone invertibility factors of the Hessian matrix at the true regression coefficients. Similar results based on an extension of the restricted eigenvalue can be also proved…

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A Unified Approach to Semiparametric Transformation Models Under General Biased Sampling Schemes

一般有偏抽样下半参数变换模型的统一方法

We propose a unified estimation method for semiparametric linear transformation models under general biased sampling schemes. The new estimator is obtained from a set of counting process-based unbiased estimating equations, developed through introducing a general weighting scheme that offsets the sampling bias. The usual asymptotic properties, including consistency and asymptotic normality, are established under suitable regularity conditions. A closed-form formula is…

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A Semiparametric Change-Point Regression Model for Longitudinal Observations

纵向观测的半参数变点回归模型

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this…

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Least Absolute Relative Error Estimation

最小绝对相对误差估计

Multiplicative regression model or accelerated failure time model, which becomes linear regression model after logarithmic transformation, is useful in analyzing data with positive responses, such as stock prices or life times, that are particularly common in economic/financial or biomedical studies. Least squares or least absolute deviation are among the most widely used criterions in statistical estimation for linear regression model. However, in many practical…

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Estimation and model selection of semiparametric multivariate survival functions under general censorship

一般删失下半参数多元生存函数的估计与模型选择

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step…

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Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests

Logistic 项目反应模型的非线性序贯设计及计算机自适应测验应用

Computerized adaptive testing is becoming increasingly popular due to advancement of modern computer technology. It differs from the conventional standardized testing in that the selection of test items is tailored to individual examinee’s ability level. Arising from this selection strategy is a nonlinear sequential design problem. We study, in this paper, the sequential design problem in the context of the logistic item response theory models. We show that the adaptive…

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On least-squares regression with censored data

删失数据的最小二乘回归

The semiparametric accelerated failure time model relates the logarithm of the failure time linearly to the covariates while leaving the error distribution unspecified. The present paper describes simple and reliable inference procedures based on the least-squares principle for this model with right-censored data. The proposed estimator of the vector-valued regression parameter is an iterative solution to the Buckley–James estimating equation with a preliminary consistent…

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A Class of Weighted Log-Rank Tests for Survival Data When the Event is Rare

稀有事件生存数据的一类加权对数秩检验

In many epidemiological and medical follow-up studies, a majority of study subjects do not experience the event of interest during the follow-up period. An important example is the ongoing prostate, lung, colorectal, and ovarian cancer Screening trial of the National Cancer Institute. In such a situation, the widely used Gp family of weighted log-rank statistics essentially reduces to the special case of the (unweighted) log-rank statistics. We propose a simple modification…

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A Class of Weighted Log-Rank Tests for Survival Data When the Event is Rare

稀有事件生存数据的一类加权对数秩检验

In many epidemiological and medical follow-up studies, a majority of study subjects do not experience the event of interest during the follow-up period. An important example is the ongoing prostate, lung, colorectal, and ovarian cancer Screening trial of the National Cancer Institute. In such a situation, the widely used Gp family of weighted log-rank statistics essentially reduces to the special case of the (unweighted) log-rank statistics. We propose a simple modification…

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Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs

固定与自适应设计下广义线性模型最大拟似然估计的强一致性

Strong consistency for maximum quasi-likelihood estimators of regression parameters in generalized linear regression models is studied. Results parallel to the elegant work of Lai, Robbins and Wei and Lai and Wei on least squares estimation under both fixed and adaptive designs are obtained. Let $y_1,\dots, y_n$ and $x_1,\dots, x_n$ be the observed responses and their corresponding design points ($p \times 1$ vectors), respectively. For fixed designs, it is shown that if the…

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A General Theory on Stochastic Curtailment for Censored Survival Data

删失生存数据随机提前终止的一般理论

Stochastic curtailment is a valuable tool in monitoring long-term medical studies. Under this approach, one calculates the conditional power, which is the probability of rejecting the null hypothesis at the scheduled end of the study given the existing data at the interim analysis, along with certain speculation about the future data. The conditional power may be used to aid the decision to terminate a study prematurely or to extend a study beyond its originally planned…

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A General Theory on Stochastic Curtailment for Censored Survival Data

删失生存数据随机提前终止的一般理论

Stochastic curtailment is a valuable tool in monitoring long-term medical studies. Under this approach, one calculates the conditional power, which is the probability of rejecting the null hypothesis at the scheduled end of the study given the existing data at the interim analysis, along with certain speculation about the future data. The conditional power may be used to aid the decision to terminate a study prematurely or to extend a study beyond its originally planned…

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Towards a general asymptotic theory for Cox model with staggered entry

分批入组 Cox 模型的一般渐近理论

A general asymptotic theory is established for the two-parameter Cox score process with staggered entry data. It extends in several directions the existing theory developed by Sellke and Siegmund, Slud and Gu and Lai. An essential tool employed here is a modern empirical process theory, as elucidated in a recent monograph by Pollard.

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A counterexample to a conjecture concerning the Hall-Wellner band

关于 Hall–Wellner 置信带猜想的反例

Hall and Wellner proposed a natural extension of the Kolmogorov-Smirnov simultaneous confidence band for survival curve using the Kaplan-Meier estimator. They and Gill conjectured that the confidence band holds for all t up to the last observed failure time. A counterexample is given herein, showing that this may not always be true.

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Semiparametric Analysis of General Additive-Multiplicative Hazard Models for Counting Processes

计数过程一般加性—乘性风险模型的半参数分析

The additive-multiplicative hazard model specifies that the hazard function for the counting process associated with a multidimensional covariate process $Z = (W^T, X^T)^T$ takes the form of $\lambda(t\mid Z) = g\{\beta^T_0 W(t)\} + \lambda_0(t)h\{\gamma^T_0X(t)\}$, where $\theta_0 = (\beta^T_0, \gamma^T_0)^T$ is a vector of unknown regression parameters, $g$ and $h$ are known link functions and $\lambda_0$ is an unspecified "baseline hazard function." In this paper, we…

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A Missing Information Principle and $M$-Estimators in Regression Analysis with Censored and Truncated Data

删失与截断回归中的缺失信息原理和 M 估计

A general missing information principle is proposed for constructing $M$-estimators of regression parameters in the presence of left truncation and right censoring on the observed responses. By making use of martingale central limit theorems and empirical process theory, the asymptotic normality of $M$-estimators is established under certain assumptions. Asymptotically efficient $M$-estimators are also developed by using data-dependent score functions. In addition,…

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