Rengarajan holds a Master of Financial Engineering - University of
California, Berkeley and he works in the investment management
industry and specializes in providing economic and investment outlook
and strategy for global equity and government bond markets. He has an
educational background in financial engineering, business, and
engineering, and professional interests include business,
finance, economics, technology and related areas.
Author: David J. Bartholomew, Martin Knott and Irini Moustaki
Latent Variable Models and Factor analysis presents a comprehensive and unified approach using a general framework to factor analysis and latent variable modeling. While the book assumes no prior background in the topic, the depth covered is impressive covering recent developments including the use of MCMC methods and structural models.
Latent variable models attempt to explain complex relations between several variables by simple relations between the variables and an underlying unobservable. The book presents a general framework to derive commonly used models and provides several numeric examples to discuss the practical applications of the techniques.
Starting with the generalized linear latent variable other models are discussed as extensions. Even the normal linear factor model is explained as a special case of the general model. The fitting of the models by maximum likelihood estimation and Bayesian methods are discussed. An important aspect of the third edition is the introduction of MCMC methods for parameter estimation.
Latent variable modeling for binary and polytomous data is covered in depth including maximum likelihood estimation of the model, fitting logit/ normal and probit/ normal models using markov chain monte carlo methods. The book highlights key issues in modeling polytomous data. Binary data is presented as a special case of polytomous. There are solid examples for both.
Latent class models introduces two class models as a latent trait model and then generalizes to k classes. The book then discusses maximum likelihood estimation, standard errors and posterior analysis. Models for manifest variables of mixed type show how modeling of data sets that contain both continuous and binary/polytomous data could be done within the generalized framework.
The final chapters deals with relationships between latent variables such as correlation between variables. The focus is on confirmatory factor analysis and linear structural relations modeling. Related techniques for investigating dependency including several ways of looking at Principal Component Analysis and estimation of correlations and regression between latent variables are covered.
Software appendix provides helpful pointers to the computer packages that have made many of the methods discussed practical. The references cover key and up to date academic research behind the techniques.
Statistical techniques to study the nature and interpretation of a latent variable should be highly useful for researchers and practitioners across several fields. The third edition of this book is comprehensive and provides a solid foundation for understanding these techniques, and is strongly recommended.
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