Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Books
  3. Handbook for Applied Modeling: Non-Gaussian and Correlated Data
  • Cited by 6
Publisher:
Cambridge University Press
Online publication date:
August 2017
Print publication year:
2017
Online ISBN:
9781316544778
DOI:
https://doi.org/10.1017/9781316544778
Subjects:
Economics, Econometrics and Mathematical Methods, Statistical Theory and Methods, Statistics and Probability
Information

Book description

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for their own data analysis needs. Data, R, and SAS scripts can be found online at http://www.spesi.org.

Reviews

'This book is a guide to modeling and analyzing non-Gaussian and correlated data. There is clearly a need for such a book to help less experienced data scientists … The data sets and models are well explained, and the limitations of each type of model on the various data sets is illustrated by frequent plots.'

Peter Rabinovitch Source: MAA Reviews

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

Contents

Contents
References
Bibliography
Agresti, A. (1998). Categorical Data Analysis, 3rd edn, John Wiley & Sons, Inc.
Agresti, A. (2007). An Introduction to Categorical Data Analysis, 2nd edn, Wiley Interscience.
Agresti, A. (2010). Analysis of Ordinal Categorical Data, 2nd edn, Wiley.
Agresti, A. and Finlay, B. (2008). Statistical Methods for the Social Sciences, 4th edn, Pearson.
Allison, P. D. (2010). Survival Analysis Using SAS: A Practical Guide, 2nd edn, SAS Institute.
Allison, P. D. (2012). Logistic Regression Using SAS: Theory and Application, SAS Institute Inc.
Beaujean, A. A. (2014). Latent Variable Modeling Using R: A Step-by-Step Guide, Taylor & Francis.
Bentler, P. M. and Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.
Bollen, K. A. (1989). Structural Equations with Latent Variables, John Wiley & Sons, Inc.
Cameron, A. C. and Trivedi, P. K. (2013). Regression Analysis of Count Data Book, 2nd edn, Cambridge University Press.
Cleveland, W. S. (1993). Visualizing Data, Hobart Press.
Collett, D. (2003). Modelling Binary Data, Chapman & Hall.
Cox, D. R. and Snell, E. G. (1989). Analysis of Binary Data, 2nd edn, Chapman and Hall.
Crawley, M. J. (2007). The R Book, 1st edn, Wiley Publishing.
Diggle, P. J., Heagerty, P., Liang, K. Y., and Zeger, S. L. (2002). Analysis of Longitudinal Data, 2nd edn, Oxford University Press.
Dobson, A. J. (2002). An Introduction to Generalized Linear Models, 3rd edn, Chapman & Hall/CRC.
Dwyer, J. H. (1983). Statistical Models for the Social and Behavioral Sciences, Oxford University Press.
Ebeling, C. E. (2004). An Introduction to Reliability and Maintainability Engineering, McGraw-Hill.
Faraway, J. J. (2006). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press.
Fitzmaurice, G. M., Laird, N. M. and Ware, J. H. (2012). Applied Longitudinal Analysis, 2nd edn, John Wiley & Sons.
Fitzmaurice, G., Davidian, M., Verbeke, G. and Molenberghs, G. (2008). Longitudinal Data Analysis, 1st edn, Chapman & Hall/CRC.
Freedman, D. A. (2009). Statistical Models: Theory and Practice, 2nd edn, Cambridge University Press.
Freedman, D. A., Pisani, R., and Purves, R. (2007). Statistics, 4th edn, W. W. Norton & Company.
Glass, G. V. and Hopkins, K. D. (1995). Statistical Methods in Education and Psychology, 3rd edn,Allyn & Bacon.
Hardin, J. W. and Hilbe, J. M. (2003). Generalized Estimating Equations, Chapman & Hall.
Hedeker, D. and Gibbons, R. D. (2006). Longitudinal Data Analysis, Wiley Interscience.
Heinze, G. and Schemper, M. (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine, 21, 2409–2419, 2002.
Hilbe, J. M. (2011). Negative Binomial Regression, Cambridge University Press.
Hilbe, J. M. (2014). Modeling Count Data, Cambridge University Press.
Hilbe, J. M. (2015). Practical Guide to Logistic Regression, Taylor & Francis.
Hosmer, D.W., Lemeshow, S., and May, S. (2008). Applied Survival Analysis, 2nd edn,Wiley Interscience.
Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X. (2013). Applied Logistic Regression, 3rd edn, Wiley.
Kleinbaum, D. G. and Klein, M. (2010). Logistic Regression: A Self-Learning Text, 3rd edn, Springer.
Kline, R. B. (2010). Principles and Practice of Structural Equation Modeling, 3rd edn, Guilford Press.
Kutner, M., Nachtsheim, C., Neter, J., and Li, W. (2004). Applied Linear Statistical Models, 5th edn, McGraw-Hill/Irwin.
Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalized Linear Models With Random Effects, Chapman & Hall/CRC.
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd edn, Chapman & Hall/CRC.
McKillup, S. (2011). Statistics Explained: An Introductory Guide for Life Scientists, 2nd edn, Cambridge University Press.
Meeker, W. Q. and Escobar, L. A. (1998). Statistical Methods for Reliability Data, Wiley.
R Core Team (2016). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. www.R-project.org.
Snedecor, G. W. and Cochran, W. G. (1989). Statistical Methods, 8th edn, Iowa State University Press.
Tabachnick, B. G. and Fidell, L. S. (2013). Using Multivariate Statistics, 6th edn, Pearson Education.
Tobias, P. A. and Trindade, D. (1995). Applied Reliability, 3rd edn, Taylor & Francis.
Trouet, V., Taylor, A. H., Wahl, E. R., Skinner, C. N., and Stephens, S. L (2010). Fire-Climate Interactions in the American West Since 1400 CE. Geophysical Research Letters, 37(4). DOI: http://10.1029/2009GL041695.
Tukey, J. W. (1977). Exploratory Data Analysis, Addison-Wesley Publishing Company.
Twisk, J. W. R. (2006). Applied Multilevel Analysis: A Practical Guide for Medical Researchers, 1st edn, Cambridge University Press.
Twisk, J. W. R. (2013<). Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, 2nd edn, Cambridge University Press.
Verzani, J. (2014). Using R for Introductory Statistics, 2nd edn, Chapman & Hall/CRC.
Vuong, Q. H (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307–333. www.jstor.org/stable/1912557.
Wood, S. (2006). Generalized Additive Models: An Introduction with R, CRC Press.
Wulczyn, E. and Taraborelli, D. (2015). Wikipedia clickstream, February, 2015. https://figshare.com/authors/Ellery_Wulczyn/689409.
Zeger, S. L. and Liang, K. Y. (1992). An overview of methods for the analysis of longitudinal data. Statistics in Medicine, 11(14–15), 1825–1839.
Ziegler, A. (2011). Generalized Estimating Equations, 1st edn, Springer.

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.