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作者：Cheng, Jerry Q.; Xie, Minge; Chen, Rong; Roberts, Fred
作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University Biomedical & Health Sciences; Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick
摘要：Potential nuclear attacks are among the most devastating terrorist attacks, with severe loss of human lives as well as damage to infrastructure. To deter such threats, it becomes increasingly vital to have sophisticated nuclear surveillance and detection systems deployed in major cities in the United States, such as New York City. In this article, we design a mobile sensor network and develop statistical algorithms and models to provide consistent and pervasive surveillance of nuclear material...
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作者：Flynn, Cheryl J.; Hurvich, Clifford M.; Simonoff, Jeffrey S.
作者单位：New York University
摘要：It has been shown that Akaike information criterion (AIC)-type criteria are asymptotically efficient selectors of the tuning parameter in nonconcave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this assumption to prove that AIC itself is asymptotically efficient and we study its performance in finite samples. In classical regression, it is known that AIC tends to select overly complex models when t...
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作者：Huang, Mian; Li, Runze; Wang, Shaoli
作者单位：Shanghai University of Finance & Economics; 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
摘要：Motivated by an analysis of U.S. house price index (HPI) data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the asce...
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作者：Langrock, Roland; Borchers, David L.; Skaug, Hans J.
作者单位：University of St Andrews; University of Bergen
摘要：We consider Markov-modulated nonhomogeneous Poisson processes for modeling sightings of marine mammals in shipboard or aerial surveys. In such surveys, detection of an animal is possible only when it surfaces, and with some species a substantial proportion of animals is missed because they are diving and thus not available for detection. This needs to be adequately accounted for to avoid biased abundance estimates. The tendency of surfacing events of marine mammals to occur in clusters motivat...
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作者：Qin, Yichen; Priebe, Carey E.
作者单位：Johns Hopkins University
摘要：We introduce a maximum Lq-likelihood estimation (MLqE) of mixture models using our proposed expectation-maximization (EM) algorithm, namely the EM algorithm with Lq-likelihood (EM-Lq). Properties of the MLqE obtained from the proposed EM-Lq are studied through simulated mixture model data. Compared with the maximum likelihood estimation (MLE), which is obtained from the EM algorithm, the MLqE provides a more robust estimation against outliers for small sample sizes. In particular, we study the...
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作者：Wang, Huixia Judy; Li, Deyuan
作者单位：North Carolina State University; Fudan University
摘要：The estimation of extreme conditional quantiles is an important issue in numerous disciplines. Quantile regression (QR) provides a natural way to capture the covariate effects at different tails of the response distribution. However, without any distributional assumptions, estimation from conventional QR is often unstable at the tails, especially for heavy-tailed distributions due to data sparsity. In this article, we develop a new three-stage estimation procedure that integrates QR and extrem...
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作者：Hahn, P. Richard; Carvalho, Carlos M.; Mukherjee, Sayan
作者单位：University of Chicago; University of Texas System; University of Texas Austin; Duke University; Duke University
摘要：We develop a modified Gaussian factor model for the purpose of inducing predictor-dependent shrinkage for linear regression. The new model predicts well across a wide range of covariance structures, on real and simulated data. Furthermore, the new model facilitates variable selection in the case of correlated predictor variables, which often stymies other methods.
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作者：Martin, Ryan; Liu, Chuanhai
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Purdue University System; Purdue University
摘要：This is to provide corrections to Theorems 1 and 3 in Martin and Liu (2013). The latter correction also casts further light on the role of nested predictive random sets.
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作者：Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Maity, Arnab; Carroll, Raymond J.
作者单位：Simon Fraser University; Texas A&M University System; Texas A&M University College Station; North Carolina State University
摘要：Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Mo...
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作者：Wu, Colin O.; Tian, Xin
作者单位：National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
摘要：An objective of longitudinal analysis is to estimate the conditional distributions of an outcome variable through a regression model. The approaches based on modeling the conditional means are not appropriate for this task when the conditional distributions are skewed or cannot be approximated by a normal distribution through a known transformation. We study a class of time-varying transformation models and a two-step smoothing method for the estimation of the conditional distribution function...
