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Books on the topic 'Multilevel models (Statistics) Markov processes'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

author, Farcomeni Alessio, and Pennoni Fulvia author, eds. Latent Markov models for longitudinal data. Boca Raton: CRC Press, 2013.

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2

Chang-Jin, Kim. Estimation of Markov regime-switching regression models with endogenous switching. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.

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3

Koroli͡uk, V. S. Stochastic models of systems. Dordrecht: Kluwer Academic Publishers, 1999.

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4

Zucchini, W. Hidden Markov models for time series: An introduction using R. Boca Raton: Chapman & Hall/CRC, 2009.

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5

M. N. M. van Lieshout. Stochastic geometry models in image analysis and spatial statistics. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1995.

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6

Lim, Kian Guan. Probability and finance theory. New Jersey: World Scientific, 2011.

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7

Probability and finance theory. Hackensack, NJ: World Scientific, 2015.

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8

Iribarren, Gonzalo Pérez. Cadenas de Markov gobernando algunos procesos aplicables a los ríos: Aplicaciones estadísticas a algunos ríos de la Región. [Montevideo: Publicaciones Matemáticas del Uruguay, 1999.

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9

Jacqueline, Gianini, ed. Modèles probabilistes d'aide à la décision. Sillery, Québec: Presses de l'Université du Québec, 1987.

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10

Haccou, Patsy. Statistical analysis of behavioural data: An approach based on time-structured models. Oxford: Oxford University Press, 1992.

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11

Mazumder, Anjali. Assessing the impact of measurement error in multilevel models via MCMC methods. 2005.

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12

Cappé, Olivier, Eric Moulines, and Tobias Ryden. Inference in Hidden Markov Models (Springer Series in Statistics). Springer, 2007.

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13

Cernat, Alexandru, and Joseph W. Sakshaug, eds. Measurement Error in Longitudinal Data. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198859987.001.0001.

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Understanding change is essential in most scientific fields. This is highlighted by the importance of issues such as shifts in public health and changes in public opinion regarding politicians and policies. Nevertheless, our measurements of the world around us are often imperfect. For example, measurements of attitudes might be biased by social desirability, while estimates of health may be marred by low sensitivity and specificity. In this book we tackle the important issue of how to understand and estimate change in the context of data that are imperfect and exhibit measurement error. The book brings together the latest advances in the area of estimating change in the presence of measurement error from a number of different fields, such as survey methodology, sociology, psychology, statistics, and health. Furthermore, it covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. The book introduces the reader to essential issues of longitudinal data collection such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also introduces the reader to some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Further, it discusses the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error.
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14

Tatarinova, Tatiana V., and Alan Schumitzky. Nonlinear Mixture Models: A Bayesian Approach. Imperial College Press, 2015.

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15

Korolyuk, Vladimir S., and Vladimir V. Korolyuk. Stochastic Models of Systems (Mathematics and Its Applications). Springer, 1999.

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16

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman and Hall/CRC, 2016.

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17

Stochastic Models In The Life Sciences And Their Methods Of Analysis. Singapore, Hong Kong: World Scientific Publishing Co. Pvt. Ltd., 2019.

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18

Hidden Markov Models for Time Series: A Practical Introduction using R (Monographs on Statistics and Applied Probability). 2nd ed. Chapman & Hall/CRC, 2009.

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19

(Editor), Persi Diaconis, and Susan Holmes (Editor), eds. Stein's Method: Expository Lectures and Applications (Institute of Mathematical Statistics, Lecture Notes-Monograph Series). Institute of Mathematical Statistics, 2004.

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20

Stein's method: Expository lectures and applications. Beachwood, Ohio: Institute of Mathematical Statistics, 2004.

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21

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

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