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基于疗效与毒性联合效用的两周期贝叶斯剂量探索AI 解读

Juhee Lee, Peter F. Thall, Yuan Ji & Peter MüllerJuhee Lee, Department of Statistics, Ohio State University, Columbus, OH 43210 (E-mail: ).Peter F. Thall, Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77230-1402 (E-mail: ).Yuan Ji, Center for Clinical and Research Informatics, North Shore University Health System, Evanston, IL 60201 (E-mail: ).Peter Müller, Department of Mathematics, University of Texas, Austin, TX 78712 (E-mail:…

超高维回归的广义信赖推断AI 解读

In recent years, the ultrahigh-dimensional linear regression problem has attracted enormous attention from the research community. Under the sparsity assumption, most of the published work is devoted to the selection and estimation of the predictor variables with nonzero coefficients. This article studies a different but fundamentally important aspect of this problem: uncertainty quantification for parameter estimates and model choices. To be more specific, this article…

通过分位数匹配实现分布匹配估计AI 解读

Motivated by the problem of selecting representative portfolios for backtesting counterparty credit risks, we propose a matching quantiles estimation (MQE) method for matching a target distribution by that of a linear combination of a set of random variables. An iterative procedure based on the ordinary least-squares estimation (OLS) is proposed to compute MQE. MQE can be easily modified by adding a LASSO penalty term if a sparse representation is desired, or by restricting…

存在缺失观测时的有效分位数回归分析AI 解读

This article examines the problem of estimation in a quantile regression model when observations are missing at random under independent and nonidentically distributed errors. We consider three approaches of handling this problem based on nonparametric inverse probability weighting, estimating equations projection, and a combination of both. An important distinguishing feature of our methods is their ability to handle missing response and/or partially missing covariates,…

基于对数正态形状混合分布的客观贝叶斯生存分析AI 解读

Survival models such as the Weibull or log-normal lead to inference that is not robust to the presence of outliers. They also assume that all heterogeneity between individuals can be modeled through covariates. This article considers the use of infinite mixtures of lifetime distributions as a solution for these two issues. This can be interpreted as the introduction of a random effect in the survival distribution. We introduce the family of shape mixtures of log-normal…

多元时间序列中多个结构断点的检测AI 解读

We propose a new nonparametric procedure (referred to as MuBreD) for the detection and estimation of multiple structural breaks in the autocovariance function of a multivariate (second-order) piecewise stationary process, which also identifies the components of the series where the breaks occur. MuBreD is based on a comparison of the estimated spectral distribution on different segments of the observed time series and consists of three steps: it starts with a consistent…

协变量同时含测量误差与误分类时的函数型和结构型方法AI 解读

Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and…

协变量自适应随机临床试验的假设检验AI 解读

Covariate-adaptive designs are often implemented to balance important covariates in clinical trials. However, the theoretical properties of conventional testing hypotheses are usually unknown under covariate-adaptive randomized clinical trials. In the literature, most studies are based on simulations. In this article, we provide theoretical foundation of hypothesis testing under covariate-adaptive designs based on linear models. We derive the asymptotic distributions of the…

分层抽样下聚类失效时间的半参数加速失效时间建模AI 解读

Clustered failure times often arise from studies with stratified sampling designs where it is desired to reduce both cost and sampling error. Semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in such settings due to lack of efficient and reliable computing routines for inferences. The challenge roots in the nonsmoothness of the rank-based estimating functions, and for clustered data, the asymptotic properties of…

基于动态切片的非参数多样本检验AI 解读

K-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An…

超高维判别分析的无模型特征筛选AI 解读

This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency…

后费舍尔实验设计：从实体实验到虚拟实验AI 解读

Fisher’s pioneering work in design of experiments has inspired further work with broader applications, especially in industrial experimentation. This article discusses three topics in physical experiments: principles of effect hierarchy, sparsity, and heredity for factorial designs, a new method called conditional main effect (CME) for de-aliasing aliased effects, and robust parameter design. I also review the recent emergence of virtual experiments on a computer. Some major…

密度调温的边际化序贯蒙特卡洛采样器AI 解读

We propose a density-tempered marginalized sequential Monte Carlo (SMC) sampler, a new class of samplers for full Bayesian inference of general state-space models. The dynamic states are approximately marginalized out using a particle filter, and the parameters are sampled via a sequential Monte Carlo sampler over a density-tempered bridge between the prior and the posterior. Our approach delivers exact draws from the joint posterior of the parameters and the latent states…

高斯动态期限结构模型的新线性估计量AI 解读

This article proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models. This new estimator is an asymptotic least-square estimator defined by the no-arbitrage conditions upon which these models are built. Further, we note that our estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches might lose their tractability. We provide an empirical application in…

局部平稳模型中局部自相关的统计推断AI 解读

For non-stationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our…

分数阶协整秩估计AI 解读

This article considers cointegration rank estimation for a p-dimensional fractional vector error correction model. We propose a new two-step procedure that allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists of estimating the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, …, p − 1. This step provides consistent estimates of the order of fractional cointegration,…

动态因子模型的识别与贝叶斯估计AI 解读

We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations and require fewer restrictions than the static factor framework. Under these restrictions, a standard structural vector autoregression (SVAR) with measurement errors can be embedded into a dynamic factor model. More generally, we also consider overidentification restrictions to achieve efficiency. We discuss general linear restrictions, either in…

利用原点矩评估多步密度预测的校准性AI 解读

The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw…

条件因子模型推断的组合方法AI 解读

This article develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature—the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional…

基于 Copula 多元模型的半参数条件分位数估计AI 解读

We consider a new approach in quantile regression modeling based on the copula function that defines the dependence structure between the variables of interest. The key idea of this approach is to rewrite the characterization of a regression quantile in terms of a copula and marginal distributions. After the copula and the marginal distributions are estimated, the new estimator is obtained as the weighted quantile of the response variable in the model. The proposed…

Let $(X_{1},Y_{1}),\ldots,(X_{n},Y_{n})$ be an i.i.d. sample from a bivariate distribution function that lies in the max-domain of attraction of an extreme value distribution. The asymptotic joint distribution of the standardized component-wise maxima $\bigvee_{i=1}^{n}X_{i}$ and $\bigvee_{i=1}^{n}Y_{i}$ is then characterized by the marginal extreme value indices and the tail copula $R$. We propose a procedure for constructing asymptotically distribution-free goodness-of-fit…

高频数据中的纯跳过程检验AI 解读

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the…

论非负无偏估计量AI 解读

We study the existence of algorithms generating almost surely nonnegative unbiased estimators. We show that given a nonconstant real-valued function $f$ and a sequence of unbiased estimators of $\lambda\in\mathbb{R}$, there is no algorithm yielding almost surely nonnegative unbiased estimators of $f(\lambda)\in\mathbb{R}^{+}$. The study is motivated by pseudo-marginal Monte Carlo algorithms that rely on such nonnegative unbiased estimators. These methods allow “exact…

时变非线性回归模型：非参数估计与模型选择AI 解读

This paper considers a general class of nonparametric time series regression models where the regression function can be time-dependent. We establish an asymptotic theory for estimates of the time-varying regression functions. For this general class of models, an important issue in practice is to address the necessity of modeling the regression function as nonlinear and time-varying. To tackle this, we propose an information criterion and prove its selection consistency…