2018

最新文章

• Hidden Population Size Estimation From Respondent-Driven Sampling: A Network Approach AI翻译
  作者：Crawford, Forrest W.; Wu, Jiacheng; Heimer, Robert
  作者单位：Yale University; Yale University; Yale University; Yale University
  摘要：Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture-recapture and multiplier methods are standard tools for inference of hidden population sizes, but they require random sampling of target population members, which is rarely possible. Respondent-driven sampling (RDS) is a survey method for hidden populations that relies on social link tracing. The RDS recruitment process is designed to spre...
• Model Selection for High-Dimensional Quadratic Regression via Regularization AI翻译
  作者：Hao, Ning; Feng, Yang; Zhang, Hao Helen
  作者单位：University of Arizona; Columbia University
  摘要：Quadratic regression (QR) models naturally extend linear models by considering interaction effects between the covariates. To conduct model selection in QR, it is important to maintain the hierarchical model structure between main effects and interaction effects. Existing regularization methods generally achieve this goal by solving complex optimization problems, which usually demands high computational cost and hence are not feasible for high-dimensional data. This article focuses on scalable...
• Dimensionality Reduction and Variable Selection in Multivariate Varying-Coefficient Models With a Large Number of Covariates AI翻译
  作者：He, Kejun; Lian, Heng; Ma, Shujie; Huang, Jianhua Z.
  作者单位：Renmin University of China; City University of Hong Kong; University of California System; University of California Riverside; Texas A&M University System; Texas A&M University College Station
  摘要：Motivated by the study of gene and environment interactions, we consider a multivariate response varying-coefficient model with a large number of covariates. The need of nonparametrically estimating a large number of coefficient functions given relatively limited data poses a big challenge for fitting such a model. To overcome the challenge, we develop a method that incorporates three ideas: (i) reduce the number of unknown functions to be estimated by using (noncentered) principal components;...
• Mixture Models With a Prior on the Number of Components AI翻译
  作者：Miller, Jeffrey W.; Harrison, Matthew T.
  作者单位：Harvard University; Brown University
  摘要：A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of componentsthat is, to use a mixture of finite mixtures (MFM). The most commonly used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces. Meanwhile, there are samplers for Dirichlet ...
• The Spike-and-Slab LASSO AI翻译
  作者：Rockova, Veronika; George, Edward I.
  作者单位：University of Chicago; University of Pennsylvania
  摘要：Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potential for penalized likelihood estimation has largely been overlooked. In this article, we bridge this gap by cross-fertilizing these two paradigms with the Spike-and-Slab LASSO procedure for variable selection and parameter estimation in linear regression. We introduce a new class of self-adaptive penalty functions that arise from a fully Bayes spike-and-slab formulation, ultimately moving beyond ...
• On the Long-Run Volatility of Stocks AI翻译
  作者：Carvalho, Carlos M.; Lopes, Hedibert F.; McCulloch, Robert E.
  作者单位：University of Texas System; University of Texas Austin; Insper; University of Chicago; Arizona State University; Arizona State University-Tempe
  摘要：In this article, we investigate whether or not the volatility per period of stocks is lower over longer horizons. Taking the perspective of an investor, we evaluate the predictive variance of k-period returns under different model and prior specifications. We adopt the state-space framework of Pastor and Stambaugh to model the dynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates. Part of the developments includes an extension that inc...
• Inference in Linear Regression Models with Many Covariates and Heteroscedasticity AI翻译
  作者：Cattaneo, Matias D.; Jansson, Michael; Newey, Whitney K.
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of California System; University of California Berkeley; Aarhus University; CREATES; Massachusetts Institute of Technology (MIT)
  摘要：The linear regression model is widely used in empirical work in economics, statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroscedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates is allowed to grow as fast as the sample size. We find that all of the usual v...
• Equivalence of Regression Curves AI翻译
  作者：Dette, Holger; Moellenhoff, Kathrin; Volgushev, Stanislav; Bretz, Frank
  作者单位：Ruhr University Bochum; University of Toronto; University of Toronto; University Toronto Mississauga; Novartis; Shanghai University of Finance & Economics
  摘要：This article investigates the problem whether the difference between two parametric models m(1), m(2) describing the relation between a response variable and several covariates in two different groups is practically irrelevant, such that inference can be performed on the basis of the pooled sample. Statistical methodology is developed to test the hypotheses H-0: d(m(1), m(2)) >= epsilon versus H-1: d(m(1), m(2)) <= epsilon to demonstrate equivalence between the two regression curves m(1), m(2)...
• The Effect of Probing Don't Know Responses on Measurement Quality and Nonresponse in Surveys AI翻译
  作者：Kuha, Jouni; Butt, Sarah; Katsikatsou, Myrsini; Skinner, Chris J.
  作者单位：University of London; London School Economics & Political Science; City St Georges, University of London
  摘要：In survey interviews, Don't know (DK) responses are commonly treated as missing data. One way to reduce the rate of such responses is to probe initial DK answers with a follow-up question designed to encourage respondents to give substantive, non-DK responses. However, such probing can also reduce data quality by introducing additional or differential measurement error. We propose a latent variable model for analyzing the effects of probing on responses to survey questions. The model makes it ...
• Disentangling Bias and Variance in Election Polls AI翻译
  作者：Shirani-Mehr, Houshmand; Rothschild, David; Goel, Sharad; Gelman, Andrew
  作者单位：Stanford University; Microsoft; Columbia University; Columbia University
  摘要：It is well known among researchers and practitioners that election polls suffer from a variety of sampling and nonsampling errors, often collectively referred to as total survey error. Reported margins of error typically only capture sampling variability, and in particular, generally ignore nonsampling errors in defining the target population (e.g., errors due to uncertainty in who will vote). Here, we empirically analyze 4221 polls for 608 state-level presidential, senatorial, and gubernatori...