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作者：Lv, Jinchi; Liu, Jun S.
作者单位：University of Southern California; Harvard University
摘要：Model selection is of fundamental importance to high dimensional modelling featured in many contemporary applications. Classical principles of model selection include the Bayesian principle and the Kullback-Leibler divergence principle, which lead to the Bayesian information criterion and Akaike information criterion respectively, when models are correctly specified. Yet model misspecification is unavoidable in practice. We derive novel asymptotic expansions of the two well-known principles in...
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作者：Imai, Kosuke; Ratkovic, Marc
作者单位：Princeton University
摘要：The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias...
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作者：Benjamini, Yoav; Bogomolov, Marina
作者单位：Tel Aviv University; Technion Israel Institute of Technology
摘要：In many complex multiple-testing problems the hypotheses are divided into families. Given the data, families with evidence for true discoveries are selected, and hypotheses within them are tested. Neither controlling the error rate in each family separately nor controlling the error rate over all hypotheses together can assure some level of confidence about the filtration of errors within the selected families. We formulate this concern about selective inference in its generality, for a very w...
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作者：Roberts, G. O.; Van Keilegom, I.
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作者：Kosmidis, Ioannis
作者单位：University of London; University College London
摘要：For the estimation of cumulative link models for ordinal data, the bias reducing adjusted score equations of Firth in 1993 are obtained, whose solution ensures an estimator with smaller asymptotic bias than the maximum likelihood estimator. Their form suggests a parameter-dependent adjustment of the multinomial counts, which in turn suggests the solution of the adjusted score equations through iterated maximum likelihood fits on adjusted counts, greatly facilitating implementation. Like the ma...
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作者：Krivitsky, Pavel N.; Handcock, Mark S.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Los Angeles
摘要：Models of dynamic networksnetworks that evolve over timehave manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and flexibility of the class of exponential family random-graph models. The modela separable temporal exponential family random-graph modelfacilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model and provide...
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作者：Lei, Jing; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：We study distribution-free, non-parametric prediction bands with a focus on their finite sample behaviour. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band by combining the idea of conformal prediction' with non-parametric conditional density estimation. The proposed estimator, called COPS (conformal optimized prediction set), always has a finite sample guarantee. Under regularity conditions the estimator converges to a...
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作者：Chambers, Ray; Chandra, Hukum; Salvati, Nicola; Tzavidis, Nikos
作者单位：University of Wollongong; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; University of Pisa; University of Southampton
摘要：Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads one to consider an outlier robust bias correction for these estimators. We develop this idea, proposing two different analytical mean-squared error estimators for the ensuing bias-corrected outlier robust estimators. Simulations based on realistic outlier-contaminated data show tha...
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作者：Kasahara, Hiroyuki; Shimotsu, Katsumi
作者单位：University of British Columbia; University of Tokyo
摘要：We analyse the identifiability of the number of components in k-variate, M-component finite mixture models in which each component distribution has independent marginals, including models in latent class analysis. Without making parametric assumptions on the component distributions, we investigate how one can identify the number of components from the distribution function of the observed data. When k2, a lower bound on the number of components (M) is non-parametrically identifiable from the r...
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作者：Goga, Camelia; Ruiz-Gazen, Anne
作者单位：Universite Bourgogne Europe; Universite de Toulouse; Universite Toulouse 1 Capitole
摘要：Currently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any non-linear parameter that is asso...
