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作者：Diciccio, Thomas J.; Young, G. Alastair
作者单位：Cornell University; Imperial College London
摘要：Objective Bayes methodology is considered for conditional frequentist inference about a canonical parameter in a multi-parameter exponential family. A condition is derived under which posterior Bayes quantiles match the conditional frequentist coverage to a higher-order approximation in terms of the sample size. This condition is on the model, not on the prior, and it ensures that any first-order probability matching prior in the unconditional sense automatically yields higher-order conditiona...
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作者：Severini, T. A.
作者单位：Northwestern University
摘要：An integrated likelihood depends only on the parameter of interest and the data, so it can be used as a standard likelihood function for likelihood-based inference. In this paper, the higher-order asymptotic properties of the signed integrated likelihood ratio statistic for a scalar parameter of interest are considered. These results are used to construct a modified integrated likelihood ratio statistic and to suggest a class of prior densities to use in forming the integrated likelihood. The ...
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作者：Czellar, Veronika; Ronchetti, Elvezio
作者单位：Hautes Etudes Commerciales (HEC) Paris; University of Geneva
摘要：In this paper we propose accurate parameter and over-identification tests for indirect inference. Under the null hypothesis the new tests are asymptotically chi(2)-distributed with a relative error of order n(-1). They exhibit better finite sample accuracy than classical tests for indirect inference, which have the same asymptotic distribution but an absolute error of order n(-1/2). Robust versions of the tests are also provided. We illustrate their accuracy in nonlinear regression, Poisson re...
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作者：Agresti, Alan; Ryu, Euijung
作者单位：State University System of Florida; University of Florida; Mayo Clinic
摘要：We propose pseudo-score confidence intervals for parameters in models for discrete data. The confidence interval is obtained by inverting a test that uses a Pearson chi-squared statistic to compare fitted values for the working model with fitted values of the model when a parameter of interest takes various fixed values. For multinomial models, the pseudo-score method simplifies to the score method when the model is saturated and otherwise it is asymptotically equivalent to score and likelihoo...
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作者：Barr, C. D.; Schoenberg, F. P.
作者单位：Harvard University; University of California System; University of California Los Angeles
摘要：The Voronoi estimator may be defined for any location as the inverse of the area of the corresponding Voronoi cell. We investigate the statistical properties of this estimator for the intensity of an inhomogeneous Poisson process, and demonstrate it is approximately unbiased with a gamma sampling distribution. We also introduce the centroidal Voronoi estimator, a simple extension based on spatial regularization of the point pattern. Simulations show the Voronoi estimator has remarkably low bia...
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作者：Greven, Sonja; Kneib, Thomas
作者单位：University of Munich; Carl von Ossietzky Universitat Oldenburg
摘要：In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, aic, have been used, based either on the marginal or on the conditional distribution. We show that the marginal aic is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional aic, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is ...
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作者：Chen, Li; Lin, D. Y.; Zeng, Donglin
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Attributable fractions are commonly used to measure the impact of risk factors on disease incidence in the population. These static measures can be extended to functions of time when the time to disease occurrence or event time is of interest. The present paper deals with nonparametric and semiparametric estimation of attributable fraction functions for cohort studies with potentially censored event time data. The semiparametric models include the familiar proportional hazards model and a broa...
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作者：Mei, Y.
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：The sequential changepoint detection problem is studied in the context of global online monitoring of a large number of independent data streams. We are interested in detecting an occurring event as soon as possible, but we do not know when the event will occur, nor do we know which subset of data streams will be affected by the event. A family of scalable schemes is proposed based on the sum of the local cumulative sum, CUSUM, statistics from each individual data stream, and is shown to asymp...
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作者：Sanderson, J.; Fryzlewicz, P.; Jones, M. W.
作者单位：University of Bristol; University of London; London School Economics & Political Science; University of Bristol
摘要：Large volumes of neuroscience data comprise multiple, nonstationary electrophysiological or neuroimaging time series recorded from different brain regions. Accurately estimating the dependence between such neural time series is critical, since changes in the dependence structure are presumed to reflect functional interactions between neuronal populations. We propose a new dependence measure, derived from a bivariate locally stationary wavelet time series model. Since wavelets are localized in ...
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作者：Fearnhead, Paul; Wyncoll, David; Tawn, Jonathan
作者单位：Lancaster University
摘要：In this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N-2) computational cost of most smoothers. The new method also overcomes some degeneracy problems in existing algorithms. Through simulation studies we show that substantial gains in efficiency are obtained for practical amounts of computational cost. It is shown both through these simulation studies, and by the analysis of an ath...
