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The middle-scale asymptotics of Wishart matrices

Wishart 矩阵的中间尺度渐近理论

We study the behavior of a real $p$-dimensional Wishart random matrix with $n$ degrees of freedom when $n,p\rightarrow\infty$ but $p/n\rightarrow0$. We establish the existence of phase transitions when $p$ grows at the order $n^{(K+1)/(K+3)}$ for every $K\in\mathbb{N}$, and derive expressions for approximating densities between every two phase transitions. To do this, we make use of a novel tool we call the $\mathcal{F}$-conjugate of an absolutely continuous distribution,…

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Simultaneous Sparse Estimation of Canonical Vectors in the p ≫ N Setting

变量数远超样本量时典型判别向量的同步稀疏估计

This article considers the problem of sparse estimation of canonical vectors in linear discriminant analysis when p ≫ N. Several methods have been proposed in the literature that estimate one canonical vector in the two-group case. However, G − 1 canonical vectors can be considered if the number of groups is G. In the multi-group context, it is common to estimate canonical vectors in a sequential fashion. Moreover, separate prior estimation of the covariance structure is…

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A Bivariate Model for Simultaneous Testing in Bioinformatics Data

生物信息学数据中用于同时检验的二元模型

We develop a novel approach for testing treatment effects in high-throughput data. Most previous works on this topic focused on testing for differences between the means, but recently it has been recognized that testing for differential variation is probably as important. We take it a step further, and introduce a bivariate model modeling strategy which accounts for both differential expression and differential variation. Our model-based approach, in which the differential…

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Improved multivariate normal mean estimation with unknown covariance when $p$ is greater than $n$

维数大于自由度时未知协方差下的多元正态均值改进估计

We consider the problem of estimating the mean vector of a $p$-variate normal $(\theta,\Sigma)$ distribution under invariant quadratic loss, $(\delta-\theta)'\Sigma^{-1}(\delta-\theta)$, when the covariance is unknown. We propose a new class of estimators that dominate the usual estimator $\delta^{0}(X)=X$. The proposed estimators of $\theta$ depend upon $X$ and an independent Wishart matrix $S$ with $n$ degrees of freedom, however, $S$ is singular almost surely when $p>n$.…

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Model Selection and Semiparametric Inference for Bivariate Failure-Time Data

二元失效时间数据的模型选择与半参数推断

We propose model selection procedures for bivariate survival models for censored data generated by the Archimedean copula family. In route to constructing the selection methodology, we develop estimates of some time-dependent association measures, including estimates of the local and global Kendall's tau, local odds ratio, and other measures defined throughout the literature. We propose a goodness-of-fit-based model selection methodology as well as a graphical approach. We…

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Approximately Exact Inference for the Common Odds Ratio in Several 2 × 2 Tables

多个二乘二表共同优势比的近似精确推断

The conditional maximum likelihood estimator (MLE) of the common odds ratio in a sequence of independent 2 × 2 tables is known to be superior to the Mantel–Haenszel estimator in terms of asymptotic efficiency and has the further advantage that its exact distribution is known. However, a long-standing barrier to the widespread use of this estimator has been computational intractability; in particular, the calculation of significance levels, confidence sets, and power based on…

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On the construction of Bayes minimax estimators

贝叶斯极小极大估计量的构造

Bayes estimation of the mean of a multivariate normal distribution is considered under quadratic loss. We show that, when particular spherical priors are used, the superharmonicity of the square root of the marginal density provides a viable method for constructing (possibly proper) Bayes (and admissible) minimax estimators. Examples illustrate the theory; most notably it is shown that a multivariate Student-$t$ prior yields a proper Bayes minimax estimate.

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Accurate Bootstrap Confidence Limits for the Cumulative Hazard and Survivor Functions under Random Censoring

随机删失下累积风险与生存函数的精确Bootstrap置信限

In clinical trials and other settings, confidence intervals for survival probabilities at a fixed point in time are often required. Typically, such intervals are constructed on the cumulative hazard scale; the quantiles required for computing these intervals are generally obtained using a first-order normal theory approximation. The appropriateness of such intervals for small sample sizes or under moderate to heavy censoring is suspect. We demonstrate that significant…

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Accurate Bootstrap Confidence Limits for the Cumulative Hazard and Survivor Functions Under Random Censoring

随机删失下累计风险与生存函数的准确自助置信限

In clinical trials and other settings, confidence intervals for survival probabilities at a fixed point in time are often required. Typically, such intervals are constructed on the cumulative hazard scale; the quantiles required for computing these intervals are generally obtained using a first-order normal theory approximation. The appropriateness of such intervals for small sample sizes or under moderate to heavy censoring is suspect. We demonstrate that significant…

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Density Estimation with Bivariate Censored Data

双变量随机删失数据的密度估计

In this article we construct a kernel estimate of the probability density function from bivariate data that have been randomly censored. We study the large-sample properties of the proposed estimator using a strong approximation result. We establish consistency and asymptotic normality and give a convenient representation of the kernel density estimator. Simulation studies show that the proposed procedure gives a good estimate of the true density function even when the…

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Practical Small-Sample Asymptotics for Regression Problems

回归问题中实用的小样本渐近近似

Saddlepoint approximations are derived for sums of independent, but not necessarily identically distributed random variables, along with generalizations to estimating equations and multivariate problems. These results are particularly useful for accurately approximating the distribution of regression coefficients. General formulas are given for the distribution of the coefficients arising out of a generalized linear model with both canonical and noncanonical link functions.…

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Quantile Comparison Functions in Two-Sample Problems, with Application to Comparisons of Diagnostic Markers

两样本分位数比较函数及其诊断标志物应用

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…

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Quantile Comparison Functions in Two-Sample Problems, With Application to Comparisons of Diagnostic Markers

两样本问题中的分位数比较函数及诊断标志物比较应用

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…

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Estimation of a Loss Function for Spherically Symmetric Distributions in the General Linear Model

一般线性模型中球对称分布下的损失估计

This paper is concerned with estimating the loss of a point estimator when sampling from a spherically symmetric distribution. We examine the canonical setting of a general linear model where the dimension of the parameter space is greater than 4 and less than the dimension of the sampling space. We consider two location estimators--the least squares estimator and a shrinkage estimator--and we compare their unbiased loss estimator with an improved loss estimator. The…

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Nonparametric Kernel Estimation in Counting Processes with Explanatory Variables

含解释变量计数过程中的非参数核估计

Cox's proportional hazards model assumes that the duration hazard rate factors into a product of a baseline hazard rate and a nonegative function of explanatory variables. An estimate of the baseline hazard rate, hence also of overall hazard rate, is proposed, based on a smoothing procedure using a kernel function. It is shown that the proposed estimator is uniformly consistent and converges weakly to a Gaussian process. Bandwidth selection issues are also discussed.

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Inference for Shift Functions in the Two-Sample Problem With Right-Censored Data: With Applications

右删失两样本问题中位移函数的推断及应用

For two distribution functions, F and G, the shift function is defined by Δ(t) = G −1 · F(t) - t. The shift function is the distance from the 45° line and the quantity plotted in Q-Q plots. In the analysis of lifetime data, A represents the difference between two treatments. The shift function can also be used to find crossing points of two distribution functions. The large-sample distribution theory for estimates of Δ is studied for right-censored data. It turns out that…

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Inference for Shift Functions in the Two-Sample Problem With Right-Censored Data: With Applications

右删失两样本问题中位移函数的推断及应用

For two distribution functions, F and G, the shift function is defined by Δ(t) = G −1 · F(t) - t. The shift function is the distance from the 45° line and the quantity plotted in Q-Q plots. In the analysis of lifetime data, A represents the difference between two treatments. The shift function can also be used to find crossing points of two distribution functions. The large-sample distribution theory for estimates of Δ is studied for right-censored data. It turns out that…

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Nonparametric kernel estimation in counting processes with explanatory variables

含解释变量的计数过程非参数核估计

SUMMARY Cox's proportional hazards model assumes that the duration hazard rate factors into a product of a baseline hazard rate and a nonnegative function of explanatory variables. An estimate of the baseline hazard rate, hence also of overall hazard rate, is proposed, based on a smoothing procedure using a kernel function. It is shown that the proposed estimator is uniformly consistent and converges weakly to a Gaussian process. Bandwidth selection issues are also discussed.

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Large Sample Theory of Spacings Statistics for Tests of Fit for the Composite Hypothesis

复合假设拟合优度检验的样本间距统计量大样本理论

SUMMARY Let X 1,…, Xn be a sequence of independent and identically distributed random variables with an unknown underlying continuous cumulative distribution function F. Often we would like to test a null hypothesis concerning the goodness of fit of F to some distribution function which is fully specified or belongs to some parametric family, i.e. in some applications the null hypothesis is simple whereas in others it may be composite. In this paper we present the large…

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Assessing the Effects of Multiple Rows on the Condition Number of a Matrix

多行观测对矩阵条件数影响的评估

Given a matrix X of n observations on k variables, it is known that the singular values of X can change substantially when few rows are omitted from X. Hadi (1988) shows that no general closed-form equation can relate the singular values of X to the singular values of X with one row deleted and gives closed-form approximations to the relationship between the singular values of the two matrices. In this article, we extend Hadi's results to the more general case of…

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Assessing the Effects of Multiple Rows on the Condition Number of a Matrix

评估多行观测对矩阵条件数的影响

Given a matrix X of n observations on k variables, it is known that the singular values of X can change substantially when few rows are omitted from X. Hadi (1988) shows that no general closed-form equation can relate the singular values of X to the singular values of X with one row deleted and gives closed-form approximations to the relationship between the singular values of the two matrices. In this article, we extend Hadi's results to the more general case of…

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