This paper presents a refined version of the M/G/infinity busy-server process described in IEEE J. Select. Areas Commun.. 16(5) (1998) 733-748 for VBR video modelling, with emphasis on a refined fitting approach of ma...
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This paper presents a refined version of the M/G/infinity busy-server process described in IEEE J. Select. Areas Commun.. 16(5) (1998) 733-748 for VBR video modelling, with emphasis on a refined fitting approach of marginal distribution. First we suggest using normal mixture distribution instead of Gamma-Pareto distribution to model the marginal distribution for greater flexibility and accuracy. Second, we retune the parameter value of the Poisson input process to ensure smooth distribution transformation. Experimental results indicate that our model gives closer estimate to the empirical cell loss behaviour than the original model. Also, its performance is more consistent than other unidistribution models like Gamma and lognormal, when fitting a wide variety of video sequences. (C) 2001 Elsevier Science B.V. All rights reserved.
We generalise the mixture autoregressive, MAR, model to the logistic mixture autoregressive with exogenous variables, LMARX, Model for the modelling of nonlinear time series. The models consist of a mixture of two Gau...
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We generalise the mixture autoregressive, MAR, model to the logistic mixture autoregressive with exogenous variables, LMARX, Model for the modelling of nonlinear time series. The models consist of a mixture of two Gaussian transfer function models with the mixing proportions changing over time. The model can also be considered as a generalisation of the self-exciting threshold autoregressive, SETAR, model and the open-loop threshold autoregressive, TARSO, model. The advantages of the LMARX model over other nonlinear time series models include a wider range of shape-changing predictive distributions, the ability to handle cycles and conditional heteroscedasticity in the time series and better point prediction. Estimation is easily done via a simple em algorithm and the model selection problem is addressed. The models are applied to two real datasets and compared with other competing models.
Estimation of tetrad crossover frequency distributions from genetic recombination data is a classic problem dating back to Weinstein (1936, Genetics 21, 155-199). But a number of important issues, such as how to speci...
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Estimation of tetrad crossover frequency distributions from genetic recombination data is a classic problem dating back to Weinstein (1936, Genetics 21, 155-199). But a number of important issues, such as how to specify the maximum number of crossovers, how to construct confidence intervals for crossover probabilities, and how to obtain correct p-values for hypothesis tests, have never been adequately addressed. In this article, we obtain some properties of the maximum likelihood estimate (MLE) for crossover probabilities that imply guidelines for choosing the maximum number of crossovers. We give these results for both normal meiosis and meiosis with nondisjunction. We also develop an accelerated em algorithm to find the MLE more efficiently. We propose bootstrap-based methods to find confidence intervals and p-values and conduct simulation studies to check the validity of the bootstrap approach.
We describe an approach to positron emission tomography, which aims to minimize the cross-entropy between the detected photon coincidence counts and the projection due to image vector, subject to the almost conservati...
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We describe an approach to positron emission tomography, which aims to minimize the cross-entropy between the detected photon coincidence counts and the projection due to image vector, subject to the almost conservation in total tube counts and nonnegativity constraints. We derive formally a fixed point iterative algorithm to solve the corresponding optimization problem. The algorithm is applied to both simulated data based on a Shepp-Logan phantom and real data, and compared with some standard algorithms, the results demonstrating its effectiveness. (C) 2001 Elsevier Science B.V. All rights reserved.
In recent work on decoding space-time codes, it is either assumed that perfect channel state information (CSI) is present, or a channel estimate is obtained using pilot symbols and then used as if it were perfect to e...
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In recent work on decoding space-time codes, it is either assumed that perfect channel state information (CSI) is present, or a channel estimate is obtained using pilot symbols and then used as if it were perfect to extract symbol estimates, In the latter case, a loss in performance is incurred, since the resulting overall receiver is not optimal. In this letter we look at maximum-likelihood (ML) sequence estimation for space-time coded systems without assuming CSI, The log-likelihood function is presented for both quasi-static and nonstatic fading channels, and an expectation-maximization (em)-based algorithm is introduced for producing ML data estimates, whose complexity is much smaller than a direct evaluation of the log-likelihood function. Simulation results indicate the em-based algorithm achieves a performance close to that of a receiver which knows the channel perfectly.
This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to t...
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This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general method for estimating generalized linear regression models with missing covariates using the em algorithm that is easily implemented when there is no auxiliary data. Vach (1997, Statistics in Medicine 16, 57-72) describes how the method can be extended when the outcome and auxiliary data are conditionally independent given the covariates in the model. The method allows the incorporation of auxiliary data without making the conditional independence assumption. We suggest tests of conditional independence and compare the performance of several estimators in an example concerning mental health service utilization in children. Using an artificial dataset, we compare the performance of several estimators when auxiliary data are available.
This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from ...
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This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed technique uses a generative latent class model to perform a probabilistic mixture decomposition. This results in a more principled approach with a solid foundation in statistical inference. More precisely, we propose to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis.
Longitudinal studies involving human participants are often complicated by subjects who do not comply with their treatment assignment or do not provide complete data. A treatment effect of interest in the presence of ...
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Longitudinal studies involving human participants are often complicated by subjects who do not comply with their treatment assignment or do not provide complete data. A treatment effect of interest in the presence of noncompliance is the complier-average causal effect (CACE;Imbens and Rubin 1997a), which is the treatment effect for subjects who would comply regardless of the assigned treatment. Imbens and Rubin (1997a,b) proposed maximum likelihood and Bayesian inferential methods for CACE, which make explicit assumptions for causal inference in the presence of noncompliance and are more efficient than standard instrumental variable methods. A model for inference about the CACE based on this approach is developed which allows for the inclusion of baseline covariates and handles missing data in the repeated outcome measures. Our methods a-re applied to a randomized trial of a job training intervention for unemployed workers. Results suggest that the intervention trial significantly reduced depression for high-risk compliers up to six months postintervention but not for low-risk compliers.
Psychological theories often posit the existence of several different states. Individuals are viewed as belonging to one of the states at a given age, but with development pass to another state. A main problem in eval...
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Psychological theories often posit the existence of several different states. Individuals are viewed as belonging to one of the states at a given age, but with development pass to another state. A main problem in evaluating such theories is representing the transition from one state to another over age. A stochastic transition framework is proposed which should be useful in many different settings. The model is illustrated with data from a cognitive development task.
This paper proposes several case-deletion measures for assessing the influence of an observation for complicated models with real missing data or hypothetical missing data corresponding to latent random variables. The...
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This paper proposes several case-deletion measures for assessing the influence of an observation for complicated models with real missing data or hypothetical missing data corresponding to latent random variables. The idea is to generalise Cook's (1977) approach to the conditional expectation of the complete-data loglikelihood function in the em algorithm. On the basis of the diagnostic measures, a procedure is proposed for detecting influential observations. Two examples illustrate our methodology. We show that the method can be applied efficiently to a wide variety of complicated problems that are difficult to handle by existing methods.
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