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作者：Villa, C.; Walker, S. G.
作者单位：University of Kent; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：We present a novel approach to constructing objective prior distributions for discrete parameter spaces. These types of parameter spaces are particularly problematic, as it appears that common objective procedures to design prior distributions are problem specific. We propose an objective criterion, based on loss functions, instead of trying to define objective probabilities directly. We systematically apply this criterion to a series of discrete scenarios, previously considered in the literat...
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作者：Chen, Yunxiao; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang
作者单位：Columbia University; University of Minnesota System; University of Minnesota Twin Cities
摘要：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 regularized maximum lik...
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作者：Vallejos, Catalina A.; Steel, Mark F. J.
作者单位：University of Warwick
摘要：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 distributions, which cov...
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作者：Calonico, Sebastian; Cattaneo, Matias D.; Titiunik, Rocio
作者单位：University of Miami; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Exploratory data analysis plays a central role in applied statistics and econometrics. In the popular regression-discontinuity (RD) design, the use of graphical analysis has been strongly advocated because it provides both easy presentation and transparent validation of the design. RD plots are nowadays widely used in applications, despite its formal properties being unknown: these plots are typically presented employing ad hoc choices of tuning parameters, which makes these procedures less au...
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作者：Martin, Ryan
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：In the frequentist program, inferential methods with exact control on error rates are a primary focus. The standard approach, however, is to rely on asymptotic approximations, which may not be suitable. This article presents a general framework for the construction of exact frequentist procedures based on plausibility functions. It is shown that the plausibility function-based tests and confidence regions have the desired frequentist properties in finite samples-no large-sample justification n...
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作者：Wang, Xueqin; Pan, Wenliang; Hu, Wenhao; Tian, Yuan; Zhang, Heping
作者单位：Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; North Carolina State University; Yale University
摘要：Statistical inference on conditional dependence is essential in many fields including genetic association studies and graphical models. The classic measures focus on linear conditional correlations and are incapable of characterizing nonlinear conditional relationship including nonmonotonic relationship. To overcome this limitation, we introduce a nonparametric measure of conditional dependence for multivariate random variables with arbitrary dimensions. Our measure possesses the necessary and...
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作者：Wu, Yuanshan; Ma, Yanyuan; Yin, Guosheng
作者单位：University of Hong Kong; Wuhan University; University of South Carolina System; University of South Carolina Columbia
摘要：Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and correct...
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作者：Tao, Ran; Zeng, Donglin; Franceschini, Nora; North, Kari E.; Boerwinkle, Eric; Lin, Dan-Yu
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of Texas System; University of Texas Health Science Center Houston
摘要：High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear reg...
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作者：Wen, Kuangyu; Wu, Ximing
作者单位：Capital University of Economics & Business; Texas A&M University System; Texas A&M University College Station; Xiamen University
摘要：The kernel density estimator (KDE) suffers boundary biases when applied to densities on bounded supports, which are assumed to be the unit interval. Transformations mapping the unit interval to the real line can be used to remove boundary biases. However, this approach may induce erratic tail behaviors when the estimated density of transformed data is transformed back to its original scale. We propose a modified, transformation-based KDE that employs a tapered and tilted back-transformation. W...
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作者：Zhu, Ke; Ling, Shiqing
作者单位：Chinese Academy of Sciences; Hong Kong University of Science & Technology
摘要：This article develops a systematic procedure of statistical inference for the auto-regressive moving average (ARMA) model with unspecified and heavy-tailed heteroscedastic noises. We first investigate the least absolute deviation estimator (LADE) and the self-weighted LADE for the model. Both estimators are shown to be strongly consistent and asymptotically normal when the noise has a finite variance and infinite variance, respectively. The rates of convergence of the LADE and the self-weighte...
