2006

最新文章

• Estimating network loss rates using active tomography AI翻译
  作者：Xi, Bowei; Michailidis, George; Nair, Vijayan N.
  作者单位：Purdue University System; Purdue University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Active network tomography refers to an interesting class of large-scale inverse problems that arise in estimating the quality of service parameters of computer and communications networks. This article focuses on estimation of loss rates of the internal links of a network using end-to-end measurements of nodes located on the periphery. A class of flexible experiments for actively probing the network is introduced, and conditions under which all of the link-level information is estimable are ob...
• Hidden Markov models for microarray time course data in multiple biological conditions - Discussion - Rejoinder AI翻译
  作者：Yuan, Ming; Kendziorski, Christina
  作者单位：University System of Georgia; Georgia Institute of Technology; University of Wisconsin System; University of Wisconsin Madison
• Post-processing posterior predictive p values AI翻译
  作者：Hjort, Nils Lid; Dahl, Fredrik A.; Hognadottir, Gunnhildur
  作者单位：University of Oslo; University of Oslo; University of Oslo
  摘要：This article addresses issues of model criticism and model comparison in Bayesian contexts, and focuses on the use of the so-called posterior predictive p values (ppp). These involve a general discrepancy or conflict measure and depend on the prior, the model, and the data. They are used in statistical practice to quantify the degree of surprise or conflict in data and to compare different combinations of prior and model. The distribution of such ppp values is far from uniform however, as we d...
• Quantile autoregression AI翻译
  作者：Koenker, Roger; Xiao, Zhijie
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Boston College
  摘要：We consider quantile autoregression (QAR) models in which the autoregressive coefficients can be expressed as monotone functions of a single, scalar random variable. The models can capture systematic influences of conditioning variables on the location, scale, and shape of the conditional distribution of the response, and thus constitute a significant extension of classical constant coefficient linear time series models in which the effect of conditioning is confined to a location shift. The m...
• A class of latent marginal models for capture-recapture data with continuous covariates AI翻译
  作者：Bartolucci, Francesco; Forcina, Antonio
  作者单位：University of Perugia
  摘要：We introduce a new family of latent class models for the analysis of capture-recapture data where continuous covariates are available. The present approach exploits recent advances in marginal parameterizations to model simultaneously, and conditionally on individual covariates, the size of the latent classes, the marginal probabilities of being captured by each list given the latent, and possible higher-order marginal interactions among lists conditionally on the latent. An EM algorithm for m...
• On the correlation matrix of the discrete Fourier transform and the fast solution of large Toeplitz systems for long-memory time series AI翻译
  作者：Chen, Willa W.; Hurvich, Clifford M.; Lu, Yi
  作者单位：Texas A&M University System; Texas A&M University College Station; New York University
  摘要：We show that for long-memory time series, the Toeplitz system Sigma(n)(f)x = b can be solved in O(n log(5/2) n) operations using a well-known version of the preconditioned conjugate gradient method, where Sigma(n)(f) is the n x n covariance matrix, f is the spectral density, and b is a known vector. Solutions of such systems are needed for optimal linear prediction and interpolation. We establish connections between this preconditioning method and the frequency domain analysis of time series. ...
• Semiparametric normal transformation models for spatially correlated survival data AI翻译
  作者：Li, Yi; Lin, Xihong
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
  摘要：There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiologic studies. In this article we propose a new class of semiparametric normal transformation models for right-censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, ...
• On convergence and bias correction of a joint estimation algorithm for multiple sinusoidal frequencies AI翻译
  作者：Song, Kai-Sheng; Li, Ta-Hsin
  作者单位：State University System of Florida; Florida State University; International Business Machines (IBM); IBM USA
  摘要：Twenty years ago, Kay proposed an iterative filtering algorithm (IFA) for jointly estimating the frequencies of multiple complex sinusoids from noisy observations. IFA is based on the fact that the noiseless signal is an autoregressive (AR) process, so the frequency estimation problem can be reformulated as the problem of estimating the AR coefficients. By iterating the cycle of AR coefficient estimation and AR filtering, IFA provides a computationally simple procedure yet capable of accurate ...
• Likelihood-based inference on haplotype effects in genetic association studies - Comment AI翻译
  作者：Satten, GA; Allen, AS; Epstein, MR
  作者单位：Centers for Disease Control & Prevention - USA; Duke University; Duke University; Emory University
• What do randomized studies of housing mobility demonstrate?: Causal inference in the face of interference AI翻译
  作者：Sobel, Michael E.
  作者单位：Columbia University
  摘要：During the past 20 years, social scientists using observational studies have generated a large and inconclusive literature on neighborhood effects. Recent workers have argued that estimates of neighborhood effects based on randomized studies of housing mobility, such as the Moving to Opportunity (MTO) demonstration, are more credible. These estimates are based on the implicit assumption of no interference between units; that is, a subject's value on the response depends only on the treatment t...