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基于断裂自适应岭回归的区间删失数据估计与变量选择AI 解读

The simultaneous estimation and variable selection for Cox model has been discussed by several authors (Fan and Li, 2002; Huang and Ma, 2010; Tibshirani, 1997) when one observes right-censored failure time data. However, there does not seem to exist an established procedure for interval-censored data, a more general and complex type of failure time data, except two parametric procedures given in Scolas et al. (2016) and Wu and Cook (2015). To address this, we propose a…

非线性模型与右删失事件时间数据的预测准确度量AI 解读

This article develops a pair of new prediction summary measures for a nonlinear prediction function with right-censored time-to-event data. The first measure, defined as the proportion of explained variance by a linearly corrected prediction function, quantifies the potential predictive power of the nonlinear prediction function. The second measure, defined as the proportion of explained prediction error by its corrected prediction function, gauges the closeness of the…

竞争风险生存数据的联合推断AI 解读

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…

纵向与聚类数据中ROC曲线非参数比较的统一方法AI 解读

We present a unified approach to nonparametric comparisons of receiver operating characteristic (ROC) curves for a paired design with clustered data. Treating empirical ROC curves as stochastic processes, their asymptotic joint distribution is derived in the presence of both between-marker and within-subject correlations. A Monte Carlo method is developed to approximate their joint distribution without involving nonparametric density estimation. The developed theory is…

区间删失数据的线性回归AI 解读

This paper concerns linear regression with interval censored data. $M$-estimators for the regression coefficients are derived. Asymptotic consistency and normality of the $M$-estimators are obtained via an exponential inequality for $U$-statistics. Asymptotically efficient estimators are provided under mild conditions.

已知总样本量时偏倚与截断数据的半参数似然推断AI 解读

Summary Biased and truncated data arise in many practical areas. Many efficient statistical methods have been studied in the literature. This paper discusses likelihood-based inferences for the two types of data in the presence of auxiliary information of known total sample size. It is shown that this information improves inference about the underlying distribution and its parameters in which we are interested. A semiparametric likelihood ratio confidence interval technique…

截断数据的半参数似然比推断AI 解读

In astronomic, demographic, epidemiologic, and other studies, the variable of interest, say the survival time, is often truncated by an associated variable. In many situations, the distribution of the truncation variable can be described by a parametric form. Unlike in the standard right-censorship model in which the censoring distribution is noninformative, knowledge of the truncation distribution can be used to improve estimation of the survival distribution. This article…

两样本分位数比较函数及其诊断标志物应用AI 解读

In this article a control percentile test, a chi-squared test, and a Kolmogorov-type test are proposed for comparing two distributions from incomplete survival data. These tests are obtained by examining a vertical shift comparison function at a single point, a finite number of points, and an entire set of points on an interval. The proposed methods also have applications in receiver operating characteristic (ROC) analysis, which has been widely used in such diverse fields…

两样本问题中的分位数比较函数及诊断标志物比较应用AI 解读

In this article a control percentile test, a chi-squared test, and a Kolmogorov-type test are proposed for comparing two distributions from incomplete survival data. These tests are obtained by examining a vertical shift comparison function at a single point, a finite number of points, and an entire set of points on an interval. The proposed methods also have applications in receiver operating characteristic (ROC) analysis, which has been widely used in such diverse fields…

不完整生存数据中分位数函数的非参数似然比置信带AI 解读

The purpose of this paper is to derive confidence bands for quantile functions using a nonparametric likelihood ratio approach. The method is easy to implement and has several appealing properties. It applies to right-censored and left-truncated data, and it does not involve density estimation or even require the existence of a density. Previous approaches (e.g., bootstrap) have imposed smoothness conditions on the density. The performance of the proposed method is…

随机截断数据下概率的非参数似然比区间估计AI 解读

We consider the problem of interval estimation of probabilities for randomly truncated data. The best-known method is based on the normal approximation of the product-limit estimator and has a drawback that it may produce intervals containing impossible values outside the range [0, 1]. Its small-sample performance is also not satisfactory. In this article we investigate an alternative approach that derives confidence intervals directly from a conditional nonparametric…

利用局部线性拟合对生命史数据进行非参数回归AI 解读

Most hazard regression models in survival analysis specify a given functional form to describe the influence of the covariates on the hazard rate. For instance, Cox's model assumes that the covariates act multiplicatively on the hazard rate, and Aalen's additive risk model stipulates that the covariates have a linear additive effect on the hazard rate. In this paper we study a fully nonparametric model which makes no assumption on the association between the hazard rate and…

广义 Pearson–Fisher 卡方拟合优度检验及生命历程数据应用AI 解读

Suppose that $X_1,\ldots,X_n$ are i.i.d. $\sim F$, and we wish to test the null hypothesis that $F$ is a member of the parametric family $\mathscr{F} = \{F_\theta(x); \theta \in \Theta\}$ where $\Theta \subset \mathbb{R}^q$. The classical Pearson-Fisher chi-square test involves partitioning the real axis into $k$ cells $I_1,\ldots, I_k$ and forming the chi-square statistic $X^2 = \sum^k_{i = 1}(O_i - nF_{\hat{\theta}}(I_i))^2/nF_{\hat{\theta}}(I_i)$, where $O_i$ is the…