1,721,012 research outputs found
Comment on the paper “On the memory complexity of the forward–backward algorithm,” by Khreich W., Granger E., Miri A., Sabourin, R.
No full textMultivariate latent Markov models with covariates for longitudinal data: Bayesian inference via Reversible Jump
No full textEvaluation of health care services through a Latent Markov Model with covariates
No full textIn this work, we focus on the evaluation of the health care services provided to elderly patients by nursing homes of four different health districts in the Umbria region (Italy). For this purpose, we analyze data coming from a longitudinal survey aimed at assessing several aspects of patient health conditions. In the analysis, we employ an extended version of the latent Markov model with covariates
that allows us to deal with dropout and non-monotone missing data, which are common in longitudinal studies. Maximum likelihood estimates are obtained by a two step approach that allows for fast estimation of the model parameters and prevents some drawbacks of the standard maximum likelihood approach encountered in the presence of many response variables and covariates. In the application to the observed data, we show how to obtain indicators of the effectiveness of the health care services delivered by each health district
A joint model for longitudinal and survival data based on an AR(1) latent process
No full textA critical problem in repeated measurement studies is the occurrence of nonignorable missing observations. A common approach to deal with this problem is joint modeling the longitudinal and survival processes for each individual on the basis of a random effect that is usually assumed to be time constant. We relax this hypothesis by introducing time-varying subject-specific random effects that follow a first-order autoregressive process, AR(1). We also adopt a generalized linear model formulation to accommodate for different types of longitudinal response (i.e. continuous, binary, count) and we consider some extended cases, such as counts with excess of zeros and multivariate outcomes at each time occasion. Estimation of the parameters of the resulting joint model is based on the maximization of the likelihood computed by a recursion developed in the hidden Markov literature. This maximization is performed on the basis of a quasi-Newton algorithm that also provides the information matrix and then standard errors for the parameter estimates. The proposed approach is illustrated through a Monte Carlo simulation study and the analysis of certain medical datasets
A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
No full textThree-step estimation of latent Markov models with covariates
No full textA three-step approach is proposed to estimate latent Markov (LM) models for longitudinal data with and without covariates. The approach is based on a preliminary clustering of sample units on the basis of time-specific responses only, and is particularly useful when a large number of response variables are observed at each time occasion. In such a context, full maximum likelihood estimation, which is typically based on the Expectation–Maximization algorithm, may have some drawbacks, essentially due to the presence of many local maxima of the model likelihood. Moreover, this algorithm may be particularly slow to converge, and may become unstable with complex LM models. The properties of the proposed estimator are illustrated theoretically and by a simulation study in which this estimator is compared with the full likelihood estimator. How reliable standard errors for the three-step parameter estimates are obtained is also shown. The approach is applied to the analysis of a dataset about the health status of elderly people resident in certain Italian nursing homes
Three step estimation of latent Markov models
No full textLatent Markov models represent a powerful tool for the analysis of longitudinal categorical data. In the presence of many response variables for each time occasion and with individual covariates, full maximum likelihood estimation of these models may present some critical aspects. Moreover, the simultaneous estimation of the parameters of the measurement model and the latent process may not be easily interpreted by applied researchers. We propose an alternative three-step approach to estimate these models, which is based on a preliminary clustering of sample units on the basis of the time-specific responses only. This method is particularly useful to overcome the drawbacks of the full likelihood approach, with an advantage both in terms of interpretability of the estimation process and in terms of computing time
A comparison of some criteria for states selection in the latent Markov model for longitudinal data
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