latent markov models for longitudinal data

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Latent Markov Models For Longitudinal Data

Author : Francesco Bartolucci
ISBN : 9781466583719
Genre : Mathematics
File Size : 89. 71 MB
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Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

Multilevel Latent Markov Models For Nested Longitudinal Discrete Data

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ISBN : 9780549463740
Genre :
File Size : 53. 55 MB
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Multilevel longitudinal data are clustered both structurally and temporally. The hierarchically nested structure induces between-subject dependency because individuals in the same unit may share something in common. The longitudinal aspect of data induces within-subject dependency because observations are made on same subjects over time. Since both types of clustered structures contribute to the dependency in the data, both aspects need to be taken into account when modeling such data. Many developments for multilevel and longitudinal data have focused on continuous response or outcome variables, and less attention has been paid to data with discrete manifest and latent variables. The Multilevel Latent Markov Model (MLMM) extends the latent class model to simultaneously incorporate the temporal and structural dependency in a single model. The MLMM is a hybrid of random-effects and conditional models. The latent Markov model, one type of conditional model, is adopted to model the change between two occasions. The random-effects modeling approach is utilized to account for the effects due to the nested structure. The parameters of the proposed model are estimated by maximum likelihood approach with modified EM procedures. Simulation studies are conducted to investigate the estimation procedures. The effects of ignoring the multilevel data structure are also studied through simulations. The estimation procedures for the MLMM are implemented in MATLAB. The MLMM MATLAB Toolbox is available for estimating the MLMM and its component models (i.e., LCM, LMM, MLCM). An application using the Educational Longitudinal Study of 2002 (ELS:2002) illustrates the usefulness of the MLMM in describing the dynamics of change. Types of random effects and several technical issues in estimation are discussed. Future extensions include incorporating covariates, relaxing the model parameters, and developing graphical representations of models and results are also proposed. The MLMM provides conceptual models and estimation tools to model movements between latent states for nested longitudinal discrete data. An MLMM allows researchers to extend the focus from individual-level to higher-level while taking into account the effects of individuals' group membership. Analyzing data using an MLMM clearly has many advantages over traditional single-level models in terms of understanding the underlying structures and the dynamics of change.

Longitudinal Research With Latent Variables

Author : Kees van Montfort
ISBN : 9783642117602
Genre : Mathematics
File Size : 87. 56 MB
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Since Charles Spearman published his seminal paper on factor analysis in 1904 and Karl Joresk ̈ og replaced the observed variables in an econometric structural equation model by latent factors in 1970, causal modelling by means of latent variables has become the standard in the social and behavioural sciences. Indeed, the central va- ables that social and behavioural theories deal with, can hardly ever be identi?ed as observed variables. Statistical modelling has to take account of measurement - rors and invalidities in the observed variables and so address the underlying latent variables. Moreover, during the past decades it has been widely agreed on that serious causal modelling should be based on longitudinal data. It is especially in the ?eld of longitudinal research and analysis, including panel research, that progress has been made in recent years. Many comprehensive panel data sets as, for example, on human development and voting behaviour have become available for analysis. The number of publications based on longitudinal data has increased immensely. Papers with causal claims based on cross-sectional data only experience rejection just for that reason.

Estimating Parameters In Markov Models For Longitudinal Studies With Missing Data Or Surrogate Outcomes

Author : Hung-Wen Yeh
ISBN : 9780549336051
Genre : Expectation-maximization algorithms
File Size : 26. 14 MB
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The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis.

Multilevel Latent Markov Models For Nested Longitudinal Discrete Data

Author : Hsiu-Ting Yu
ISBN : OCLC:262535980
Genre :
File Size : 58. 95 MB
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Longitudinal Data Analysis

Author : Catrien C. J. H. Bijleveld
ISBN : 0761955372
Genre : Computers
File Size : 26. 20 MB
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All the main approaches to longitudinal data analysis are covered in this guide that enables the reader to understand and choose an appropriate technique when dealing with a specific research problem.

An Introduction To Longitudinal Research

Author : Elisabetta Ruspini
ISBN : 9781134510627
Genre : Reference
File Size : 64. 66 MB
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One of the major changes in the social science research landscape in recent years has been the introduction of computerised panel surveys in Europe and the US which make longitudinal data widely available to graduate students for the first time. Elisabetta Ruspini here provides a concise yet comprehensive introduction to the issues involved in this kind of research. This book: * Defines the concept of longitudinal research * Gives guidance on sources of longitudinal data in Europe and the US and their strengths and weaknesses * Discusses the choices that need to be made in this kind of research - for instance the advantages and disadvantages of certain types of research data and of different types of analysis * Highlights some of the problems involved, e.g. the issue of comparability within longitudinal research

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