-
作者：Crawford, Forrest W.; Minin, Vladimir N.; Suchard, Marc A.
作者单位：Yale University; University of Washington; University of Washington Seattle; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
摘要：Birth-death processes (BDPs) are continuous-time Markov chains that track the number of particles in a system over time. While widely used in population biology, genetics, and ecology, statistical inference of the instantaneous particle birth and death rates remains largely limited to restrictive linear BDPs in which per-particle birth and death rates are constant. Researchers often observe the number of particles at discrete times, necessitating data augmentation procedures such as expectatio...
-
作者：Zheng, Shuzhuan; Yang, Lijian; Haerdle, Wolfgang K.
作者单位：Michigan State University; Soochow University - China; Humboldt University of Berlin; Singapore Management University
摘要：Functional data analysis (FDA) has become an important area of statistics research in the recent decade, yet a smooth simultaneous confidence corridor (SCC) does not exist in the literature for the mean function of sparse functional data. SCC is a powerful tool for making statistical inference on an entire unknown function, nonetheless classic Hungarian embedding techniques for establishing asymptotic correctness of SCC completely fail for sparse functional data. We propose a local linear SCC ...
-
作者：Ing, Ching-Kang; Yang, Chiao-Yi
作者单位：Academia Sinica - Taiwan; National Taiwan University
摘要：Let observations y(1),..., y(n) be generated from a first-order autoregressive (AR) model with positive errors. In both the stationary and unit root cases, we derive moment bounds and limiting distributions of an extreme value estimator, rho(n), of the AR coefficient. These results enable us to provide asymptotic expressions for the mean squared error (MSE) of rho n and the mean squared prediction error (MSPE) of the corresponding predictor, y(n+1), of y(n+1) Based on these expressions, we com...
-
作者：Liu, Jingyuan; Li, Runze; Wu, Rongling
作者单位：Xiamen University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health
摘要：This article is concerned with feature screening and variable selection for varying coefficient models with ultrahigh-dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterati...
-
作者：Chan, Ngai Hang; Yau, Chun Yip; Zhang, Rong-Mao
作者单位：Chinese University of Hong Kong; Renmin University of China; Zhejiang University
摘要：Consider a structural break autoregressive (SBAR) process Y-1 = Sigma(m+1)(j=1) (sic)beta jT Yt-1 + sigma(Yt-1, ...,Yt-q)epsilon 1(sic) I(t(j-1) <= t < t(1) < ... < t(m) vertical bar 1 = n + 1, sigma (.) is a measurable function on R-q, and {epsilon(t)} are white noise with unit variance. In practice, the number of change-points m is usually assumed to be known and small, because a large m would involve a huge amount of computational burden for parameters estimation. By reformulating the probl...
-
作者：Delaigle, Aurore; Hall, Peter
作者单位：University of Melbourne; University of California System; University of California Davis
摘要：Nonparametric estimation of a density from contaminated data is a difficult problem, for which convergence rates are notoriously slow. We introduce parametrically assisted nonparametric estimators which can dramatically improve on the performance of standard nonparametric estimators when the assumed model is close to the true density, without degrading much the quality of purely nonparametric estimators in other cases. We establish optimal convergence rates for our problem and discuss estimato...
-
作者：Carone, Marco; Asgharian, Masoud; Jewell, Nicholas P.
作者单位：University of Washington; University of Washington Seattle; University of California System; University of California Berkeley; McGill University
摘要：Dementia is one of the world's major public health challenges. The lifetime risk of dementia is the proportion of individuals who ever develop dementia during their lifetime. Despite its importance to epidemiologists and policy-makers, this measure does not seem to have been estimated in the Canadian population. Data from a birth cohort study of dementia are not available. Instead, we must rely on data from the Canadian Study of Health and Aging, a large cross-sectional study of dementia with ...
-
作者：Abadie, Alberto; Imbens, Guido W.; Zheng, Fanyin
作者单位：Harvard University; National Bureau of Economic Research; Stanford University; Harvard University
摘要：Following the work by Eicker, Huber, and White it is common in empirical work to report standard errors that are robust against general misspecification. In a regression setting, these standard errors are valid for the parameter that minimizes the squared difference between the conditional expectation and a linear approximation, averaged over the population distribution of the covariates. Here, we discuss an alternative parameter that corresponds to the approximation to the conditional expecta...
-
作者：Ning, Jing; Qin, Jing; Shen, Yu
作者单位：University of Texas System; UTMD Anderson Cancer Center; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
摘要：The semiparametric accelerated failure time (AFT) model is one of the most popular models for analyzing time-to-event outcomes. One appealing feature of the AFT model is that the observed failure time data can be transformed to identically independent distributed random variables without covariate effects. We describe a class of estimating equations based on the score functions for the transformed data, which are derived from the full likelihood function under commonly used semiparametric mode...
-
作者：Li, Bing; Chun, Hyonho; Zhao, Hongyu
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Purdue University System; Purdue University; Yale University
摘要：We introduce a nonparametric method for estimating non-Gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a...
