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作者：Avella-Medina, Marco; Ronchetti, Elvezio
作者单位：Massachusetts Institute of Technology (MIT); University of Geneva
摘要：Generalized linear models are popular for modelling a large variety of data. We consider variable selection through penalized methods by focusing on resistance issues in the presence of outlying data and other deviations from assumptions. We highlight the weaknesses of widely-used penalized M-estimators, propose a robust penalized quasilikelihood estimator, and show that it enjoys oracle properties in high dimensions and is stable in a neighbourhood of the model. We illustrate its finite-sampl...
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作者：Lunardon, N.
作者单位：University of Milano-Bicocca
摘要：Firth (1993) introduced a method for reducing the bias of the maximum likelihood estimator. Here it is shown that the approach is also effective in reducing the sensitivity of inferential procedures to incidental parameters.
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作者：Chen, Yong; Huang, Jing; Ning, Yang; Liang, Kung-Yee; Lindsay, Bruce G.
作者单位：University of Pennsylvania; Cornell University; National Yang Ming Chiao Tung University
摘要：Composite likelihood has been widely used in applications. The asymptotic distribution of the composite likelihood ratio statistic at the boundary of the parameter space is a complicated mixture of weighted chi(2) distributions. In this paper we propose a conditional test with data-dependent degrees of freedom. We consider a modification of the composite likelihood which satisfies the second-order Bartlett identity. We show that the modified composite likelihood ratio statistic given the numbe...
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作者：Heard, N. A.; Rubin-Delanchy, P.
作者单位：Imperial College London; University of Bristol
摘要：Combining p-values from independent statistical tests is a popular approach to meta-analysis, particularly when the data underlying the tests are either no longer available or are difficult to combine. Numerous p-value combination methods appear in the literature, each with different statistical properties, yet often the final choice used in a meta-analysis can seem arbitrary, as if all effort has been expended in building the models that gave rise to the p-values. Birnbaum (1954) showed that ...
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作者：Kang, Jian; Reich, Brian J.; Staicu, Ana-Maria
作者单位：University of Michigan System; University of Michigan; North Carolina State University
摘要：This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational algorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian process prior leads to the posterior...
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作者：Miratrix, L. W.; Wager, S.; Zubizarreta, J. R.
作者单位：Harvard University; Stanford University; Harvard University; Harvard Medical School
摘要：Estimating a population mean from a sample obtained with unknown selection probabilities is important in the biomedical and social sciences. Using a ratio estimator, Aronow & Lee (2013) proposed a method for partial identification of the mean by allowing the unknown selection probabilities to vary arbitrarily between two fixed values. In this paper, we show how to use auxiliary shape constraints on the population outcome distribution, such as symmetry or log-concavity, to obtain tighter bounds...
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作者：Zheng, Yao; Li, Wai Keung; Li, Guodong
作者单位：University of Hong Kong
摘要：The estimation of time series models with heavy-tailed innovations has been widely discussed, but corresponding goodness-of-fit tests have attracted less attention, primarily because the autocorrelation function commonly used in constructing goodness-of-fit tests necessarily imposes certain moment conditions on the innovations. As a bounded random variable has finite moments of all orders, we address the problem by first transforming the residuals with a bounded function. More specifically, we...
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作者：Hong, L.; Kuffner, T. A.; Martin, R.
作者单位：Robert Morris University; Washington University (WUSTL); North Carolina State University
摘要：In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected submodel, may not be valid because it ignores the selected submodel's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.
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作者：Mao, Lu
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：We introduce a general class of causal estimands which extends the familiar notion of average treatment effect. The class is defined by a contrast function, prespecified to quantify the relative favourability of one outcome over another, averaged over the marginal distributions of two potential outcomes. Natural estimators arise in the form of U-statistics. We derive both a naive inverse propensity score weighted estimator and a class of locally efficient and doubly robust estimators. The usef...
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作者：Chung, Yunro; Ivanova, Anastasia; Hudgens, Michael G.; Fine, Jason P.
作者单位：Fred Hutchinson Cancer Center; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：We consider the estimation of the semiparametric proportional hazards model with an unspecified baseline hazard function where the effect of a continuous covariate is assumed to be monotone. Previous work on nonparametric maximum likelihood estimation for isotonic proportional hazard regression with right-censored data is computationally intensive, lacks theoretical justification, and may be prohibitive in large samples. In this paper, partial likelihood estimation is studied. An iterative qua...
