A new robust, low complexity algorithm for multiuser tracking is proposed, modifying the two-stage parallel architecture of the estimate-maximize (em) algorithm, The algorithm copes with spatially colored noise, large...
详细信息
A new robust, low complexity algorithm for multiuser tracking is proposed, modifying the two-stage parallel architecture of the estimate-maximize (em) algorithm, The algorithm copes with spatially colored noise, large differences in source powers, multipath, and crossing trajectories, Following a discussion on stability, the simulations demonstrate an asymptotic and tracking behavior that neither the em nor a nonparallelized tracker can emulate.
In modelling the succession of states occupied by individuals over time, it is important to include state dependence, duration-of-stay effects, and variation between individuals over and above that explained by covari...
详细信息
In modelling the succession of states occupied by individuals over time, it is important to include state dependence, duration-of-stay effects, and variation between individuals over and above that explained by covariates. It has been long recognized that omission of any one of these characteristics can result in seriously misleading inference about the other two and the effects of covariates. Mixed Markov renewal models are the most parsimonious general class of model which incorporate all three characteristics. However, problems over the detailed specification and the complex computational requirements of such models have inhibited their use in social science research. This paper is concerned with one specific problem: ensuring sufficient flexibility for the multivariate mixing distribution by correcting the tendency to under predict the number who remain in the same state throughout. It is shown how an efficient em-type algorithm based on weighted GLMs is readily extended to include stayers within a mixed Markov renewal formulation. Ln contrast to the direct maximization of the log-likelihood using a Newton-Raphson (or similar) algorithm, stayers may improve significantly the convergence performance of the em-GLM approach. (C) 1997 Elsevier Science B.V.
The utility of blinded sample size re-estimation for clinical trials depends on the ability to estimate variability without providing information about the true treatment difference, and on some reasonable assurance t...
详细信息
The utility of blinded sample size re-estimation for clinical trials depends on the ability to estimate variability without providing information about the true treatment difference, and on some reasonable assurance that the method is not likely to cause the sample size to be increased when the treatment effect is better than anticipated. We show that violations of these properties are unlikely to occur in practice.
In biomedical research and diagnostic practice it is common to classify objects dichotomously based on continuous observations (x) measuring some form of biological activity, where some proportion of the objects have ...
详细信息
In biomedical research and diagnostic practice it is common to classify objects dichotomously based on continuous observations (x) measuring some form of biological activity, where some proportion of the objects have a level of activity above background. In this paper, we consider the problem of estimating the proportion of positive objects for a typical assay where: (i) the distribution of x for positive objects is unknown, although (ii) the risk of positivity is known to be a monotonic function of x;and (iii) x has been measured for a set of negative control objects. Monte Carlo simulations evaluating four alternative estimators of the positivity, including novel non-parametric mixture decompositions, indicate that where the positives and negatives have distributions of x with a moderate degree of overlap, a non-parametric decomposition using a latent class model provides precise and close to unbiased estimates. The methods are illustrated using data from an autoradiography assay used in cell biology.
Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variab...
详细信息
Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable-the hidden state. We discuss a generalization of HMMs in which this state is factor-ed into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm far inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward-backward algorithm for HMMs and to algorithms for more general graphical models. Doe to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or Variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach's chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot.
In this paper, we study the maximum likelihood estimation of a model with mixed binary responses and censored observations. The model is very general and includes the Tobit model and the binary choice model as special...
详细信息
In this paper, we study the maximum likelihood estimation of a model with mixed binary responses and censored observations. The model is very general and includes the Tobit model and the binary choice model as special cases. We show that, by using additional binary choice observations, our method is more efficient than the traditional Tobit model. Two iterative procedures are proposed to compute the maximum likelihood estimator (MLE) for the model based on the em algorithm (Dempster et al, 1977) and the Newton-Raphson method. The uniqueness of the MLE is proved. The simulation results show that the inconsistency and inefficiency call he significant when the Tobit method is applied to the present mixed model. The experiment results also suggest that the em algorithm is much faster than the Newton-Raphson method for the present mixed model. The method also allows one to combine two data sets, the smaller data set with more detailed observations and the larger data set with less detailed binary choice observations in order to improve the efficiency of estimation. This may entail substantial savings when one conducts surveys.
The relationship between a longitudinal covariate and a failure time process can be assessed using the Cox proportional hazards regression model. We consider the problem of estimating the parameters in the Cox model w...
详细信息
The relationship between a longitudinal covariate and a failure time process can be assessed using the Cox proportional hazards regression model. We consider the problem of estimating the parameters in the Cox model when the longitudinal covariate is measured infrequently and with measurement error. We assume a repeated measures random effects model for the covariate process. Estimates of the parameters are obtained by maximizing the joint likelihood for the covariate process and the failure time process. This approach uses the available information optimally because we use both the covariate and survival data simultaneously. Parameters are estimated using the expectation-maximization algorithm. We argue that such a method is superior to naive methods where one maximizes the partial likelihood of the Cox model using the observed covariate values. It also improves on two-stage methods where, in the first stage, empirical Bayes estimates of the covariate process are computed and then used as time-dependent covariates in a second stage to find the parameters in the Cox model that maximize the partial likelihood.
The probit-normal model for binary data (McCulloch, 1994, Journal of the American Statistical Association 89;330-335) is extended to allow correlated random effects. To obtain maximum likelihood estimates, we use the ...
详细信息
The probit-normal model for binary data (McCulloch, 1994, Journal of the American Statistical Association 89;330-335) is extended to allow correlated random effects. To obtain maximum likelihood estimates, we use the em algorithm with its M-step greatly simplified under the assumption of a probit link and its E-step made feasible by Gibbs sampling. Standard errors are calculated by inverting a Monte Carlo approximation of the information matrix rather than via the Sem algorithm. A method is also suggested that accounts for the Monte Carlo variation explicitly. As an illustration, we present a new analysis of the famous salamander mating data. Unlike previous analyses, we find it necessary to introduce different variance components for different species of animals. Finally, we consider models with correlated errors as well as correlated random effects.
In this paper, parametric and nonparametric estimators of the stress-strength reliability R = P(Y < X) are obtained and compared when the random variables X and Y are independent and each of which is a mixture of l...
详细信息
In this paper, parametric and nonparametric estimators of the stress-strength reliability R = P(Y < X) are obtained and compared when the random variables X and Y are independent and each of which is a mixture of lognormal components. 100(1 - alpha)% confidence bounds are obtained and compared in both of the parametric and nonparametric cases. Simulation shows that the parametric point estimates are better than the nonparametric point estimates for all sample sizes. This is also true for interval estimates, particularly when the sample size N is small. As N increases, no great loss in precision occurs if Govindarajulu's bounds are used rather than the parametric bounds. The nonparametric bounds are simpler and faster to obtain.
作者：
Kim, DKYonsei Univ
Coll Med Dept Biostat Seodaemoon Gu Seoul 120752 South Korea
We considered the regression analysis of the event time data with left-, right-, or interval-censored observations. We extended life-table techniques for censored survival data using log-linear models to incorporate i...
详细信息
We considered the regression analysis of the event time data with left-, right-, or interval-censored observations. We extended life-table techniques for censored survival data using log-linear models to incorporate interval-censored failures. The em algorithm was used to calculate maximum likelihood estimates for the parameters. We assumed that the hazard function was a stepwise function over disjoint intervals of time;thus, the nonparametric model, the parametric exponential model, and the semiparametric Cox proportional hazard model were easily implemented as special cases. We adapted the restricted em algorithm to test hypotheses and to construct confidence intervals for the parameters. These methods were applied in an analysis of the recurrence time for treated melanoma patients.
暂无评论