Handling missing data based on parametric models typically involves computing the conditional expectation of missing data given observed data for nonrespondents. Under a nonignorable missing data mechanism, the condit...
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Handling missing data based on parametric models typically involves computing the conditional expectation of missing data given observed data for nonrespondents. Under a nonignorable missing data mechanism, the conditional distribution requires joint modeling of the study and response variables. A natural way of factoring the model is to use models for the distribution of variables under complete response and for the probability of response. Sensitivity to model specification is a serious scientific problem. Models cannot be validated, however, from missing data, because, by definition, the information needed for validation is missing. In many cases, under assumed models, a Monte Carlo (MC) method can be used to compute the conditional expectation of missing given observed variables. The issue of model specification translates into the questions, how should one generate values from the conditional distribution for nonrespondents? One way to interpret this issue is as the need to specify an imputation method for the missing data. In this paper, we consider a simulation method based on the model for the distribution of respondents together with the Sampling Importance Resampling (sir) algorithm. The proposed method is shown to be more robust than some current approaches in the sense that assumed models can be verified from respondents. A linearized variance estimation method is also studied. Results from a limited simulation study are presented. (C) 2013 Elsevier B.V. All rights reserved.
A spaceborne microwave radiometer has a low spatial resolution limited by its antenna size. Enhancing the spatial resolution of data acquired by such sensors can improve the quality of subsequent applications. To impr...
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A spaceborne microwave radiometer has a low spatial resolution limited by its antenna size. Enhancing the spatial resolution of data acquired by such sensors can improve the quality of subsequent applications. To improve the spatial resolution of the Microwave Radiation Imager (MWRI) onboard the Fengyun 3D satellite, this study used a Scatterometer Image Reconstruction (sir) algorithm to generate resolution-enhanced swath brightness temperature data based on redundant information from overlaps between scanning points. These swath data have a higher pixel resolution that can reach 1/4 of the sampling frequency. The quality of reconstructed images, evaluated through visual comparison and quantitative analysis, revealed reasonable potential for providing more detailed depictions of surface information. Statistical analysis revealed a lower root mean square deviation of 0.8 K and a bias of 0.04 K following the sir process. Analysis of the pixel spatial response function confirmed that the enhanced data have substantially finer spatial resolution than that of Level-1 data for 10-89 GHz vertical/horizontal channels, with an improvement of 9-39% in effective resolution. The findings of this study show that the sir algorithm has potential for enhancing the quality of MWRI data and for widening the application domain to satellite product development, satellite data assimilation for numerical weather prediction, and other related fields.
Monte Carlo methods deal with generating random variates from probability density functions in order to estimate unknown parameters or general functions of unknown parameters and to compute their expected values, vari...
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Monte Carlo methods deal with generating random variates from probability density functions in order to estimate unknown parameters or general functions of unknown parameters and to compute their expected values, variances and covariances. One generally works with the multivariate normal distribution due to the central limit theorem. However, if random variables with the normal distribution and random variables with a different distribution are combined, the normal distribution is not valid anymore. The Monte Carlo method is then needed to get the expected values, variances and covariances for the random variables with distributions different from the normal distribution. The error propagation by Monte Carlo methods is discussed and methods for generating random variates from the multivariate normal distribution and from the multivariate uniform distribution. The Monte Carlo integration is presented leading to the sampling-importance-resampling algorithm. Markov chain Monte Carlo methods provide by the Metropolis algorithm and the Gibbs sampler additional ways of generating random variates. A special topic is the Gibbs sampler for computing and propagating large covariance matrices. This task arises, for instance, when the geopotential is determined from satellite observations. The example of the minimal detectable outlier shows, how the Monte Carlo method is used to determine the power of a hypothesis test.
We consider the problem of inference about a quantity of interest given different sources of information linked by a deterministic population dynamics model. Our approach consists of translating all the available info...
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We consider the problem of inference about a quantity of interest given different sources of information linked by a deterministic population dynamics model. Our approach consists of translating all the available information into a joint premodel distribution on all the model inputs and outputs and then restricting this to the submanifold defined by the model to obtain the joint postmodel distribution. Marginalizing this yields inference, conditional on the model, about quantities of interest, which can be functions of model inputs, model outputs, or both. Samples from the postmodel distribution are obtained by importance sampling and Rubin's sir algorithm. The framework includes as a special case the situation where the pre-model information about the outputs consists of measurements with error; this reduces to standard Bayesian inference. The results are in the form of a sample from the postmodel distribution and so can be examined using the full range of exploratory data analysis techniques. Methods for comparing competing population dynamics models are developed, based on a generalization of the Bayes factor idea. A key quantity used by the International Whaling Commission (IWC) in making decisions about bowhead whales,Balaena mysticetus, is the replacement yield, RY. Information about the species is of three main types: recent census information, historical catch records, and evidence about birth and death rates. These are combined using a special case of the Leslie matrix population dynamics model. Our method yields full inference about RY and also sheds light on other, sometimes controversial, questions of scientific interest. These ideas are also applicable to many simulation models in other areas of science and policy making. Software to implement these methods is available from StatLib.
This paper presents two manifold training techniques to reconstruct high dynamic range images from a set of low dynamic range images which have different exposure times. It provides the performance oil noisy images.
ISBN:
(纸本)9783540877332
This paper presents two manifold training techniques to reconstruct high dynamic range images from a set of low dynamic range images which have different exposure times. It provides the performance oil noisy images.
Sample survey designs in which each participant is administered a subset of the items contained in a complete survey instrument are becoming an increasingly popular method of reducing respondent burden (Mislevy, Beate...
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Sample survey designs in which each participant is administered a subset of the items contained in a complete survey instrument are becoming an increasingly popular method of reducing respondent burden (Mislevy, Beaten, Kaplan, & Sheehan, 1992;Raghunathan & Grizzle, 1995: Wacholder, Carroll, Pee, & Gall, 1994). Data from these survey designs can be analyzed using multiple imputation methodology that generates several imputed values for the missing data and thus yields several complete data sets. These data sets are then analyzed using complete data estimators and their standard errors (Rubin, 1987b). Generating the imputed data sets, however, can be very difficult. We describe improvements to the methods currently used to generate the imputed data sets for item response models summarizing educational data collected by the National Assessment of Educational Progress (NAEP), an ongoing collection of samples of 4th, 8th, and 12th grade students in the United States. The improved approximations produce small to moderate changes in commonly reported estimates, with the larger changes associated with an increasing amount of missing data. The improved approximations produce larger standard errors.
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