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作者：Balabdaoui, Fadoua; Jankowski, Hanna; Rufibach, Kaspar; Pavlides, Marios
作者单位：Universite PSL; Universite Paris-Dauphine; York University - Canada; University of Zurich; Queens University Belfast
摘要：The assumption of log-concavity is a flexible and appealing non-parametric shape constraint in distribution modelling. In this work, we study the log-concave maximum likelihood estimator of a probability mass function. We show that the maximum likelihood estimator is strongly consistent and we derive its pointwise asymptotic theory under both the well-specified and misspecified settings. Our asymptotic results are used to calculate confidence intervals for the true log-concave probability mass...
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作者：Krivobokova, Tatyana
作者单位：University of Gottingen
摘要：There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated by minimizing criteria that approximate the average mean-squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a realization of some stochastic process. The asymptotic properties of both smoothing parameter estimators for penalized splines are studied and compare...
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作者：Ramsahai, Roland R.
作者单位：University of Cambridge
摘要：Interdependent effects are usually distinguished from statistical interaction by using the sufficient causes framework. This almost always involves expressing probability distributions as deterministic logic functions, where certain conditions invariably produce or prevent an outcome. Using an idea from the philosophy literature, that a cause is defined as an event which increases the probability of an outcome, a probabilistic sufficient causes framework is developed here. It expresses distrib...
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作者：Fan, Jianqing; Liao, Yuan; Mincheva, Martina
作者单位：Princeton University; University System of Maryland; University of Maryland College Park; Princeton University
摘要：The paper deals with the estimation of a high dimensional covariance with a conditional sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the principal orthogonal complement thresholding method POET' to explore such an approximate factor structure with sparsity. The POET-estimator includes...
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作者：Sangalli, Laura M.; Ramsay, James O.; Ramsay, Timothy O.
作者单位：Polytechnic University of Milan; McGill University; University of Ottawa; Ottawa Hospital Research Institute
摘要：We describe a model for the analysis of data distributed over irregularly shaped spatial domains with complex boundaries, strong concavities and interior holes. Adopting an approach that is typical of functional data analysis, we propose a spatial spline regression model that is computationally efficient, allows for spatially distributed covariate information and can impose various conditions over the boundaries of the domain. Accurate surface estimation is achieved by the use of piecewise lin...
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作者：Evans, Robin J.; Richardson, Thomas S.
作者单位：University of Cambridge; University of Washington; University of Washington Seattle
摘要：Marginal log-linear (MLL) models provide a flexible approach to multivariate discrete data. MLL parameterizations under linear constraints induce a wide variety of models, including models that are defined by conditional independences. We introduce a subclass of MLL models which correspond to acyclic directed mixed graphs under the usual global Markov property. We characterize for precisely which graphs the resulting parameterization is variation independent. The MLL approach provides the firs...
