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Statistics bibliography 14 - stats

Abreu, M.N.S. et al. (2008) Ordinal logistic regression models: application in quality of life studies. Cadernos de Saúde Pública 24, Sup 4, S581-S591. [free pdf]


Akaike, H. (1992). Information theory and an extension of the maximum likelihood principle. In Kotz, S. & Johnson, N.L. (eds) Breakthroughs in Statistics, Vol1, Foundations and Basic Theory. Springer Verlag, New York.


Austin, P.C. & Tua, J.V. (2004). Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. Journal of Clinical Epidemiology 57, 1138-1146. [free pdf]


Begg, M.D. & Lagakos, S. (1990). On the consequences of model misspecification in logistic regression. Environmental Health Perspectives 87, 69-75. [free pdf]


Bender, R. & Benner, A. (2000). Calculating ordinal regression models in SAS and S-Plus. Biometrical Journal 42 (6), 677-679. [free pdf]


Bender, R. & Grouven, U. (1996). Logistic regression models used in medical research are poorly presented. (Letter) BMJ 313, 628 (7 September). [free pdf]


Beyene, J. et al. (2005). Determining relative importance of variables in developing and validating predictive models. BMC Medical Research Methodology 9: 64. [free pdf]


Breiman, L. (1992). The little bootstrap and other methods for dimensionality selection in regression: X-fixed prediction error . Journal of the American Statistical Association 87, No. 419, 738-754.


Bender, R. & Grouven, U. (1998) Using binary logistic regression models for ordinal data with non-proportional odds Journal of Clinical Epidemiology 51 (10), 809-816. [free pdf]


Biesheuvel, C.J. et al. (2008). Polytomous logistic regression analysis could be applied more often in diagnostic research. Journal of Clinical Epidemiology 61 (2), 125-134.


Bradburn, M.J. et al. (2003) . Survival Analysis Part II: Multivariate data analysis - an introduction to concepts and methods. British Journal of Cancer 89, 431 - 436. [free pdf]


Bradburn, M.J. et al. (2003) Survival Analysis Part III: Multivariate data analysis - choosing a model and assessing its adequacy and fit. British Journal of Cancer 89, 605 - 611. [free pdf]


Breslow, N.E. & Day, N.E. (1980). Statistical methods in cancer research Volume 1 - The analysis of case-control studies. Chapter 7: Conditional logistic regression for matched sets. IARC Scientific Publications No. 32, WHO, Lyon. [free pdf]


Bring, J. et al. (1994). How to standardize regression coefficients. The American Statistician 48 (3), 209-213.


Burnham, K.P. & Anderson, D.R. (2002). Model selection and multimodel inference: a practical information-theoretic approach. Springer, 488 pp.


Chan, Y.H.. (2004). Biostatistics 203. Survival analysis. Singapore Medical Journal 45 (6), 249-256. [free pdf]


Chevan, A. & Sutherland, M. (1991). Hierarchical partitioning. The American Statistician 45 (2), 90-96.


Clark, T.G. et al. (2003a). Survival Analysis Part I: Basic concepts and first analyses British Journal of Cancer 89, 232 - 238. [free pdf]


Clark, T.G. et al. (2003b). Survival Analysis Part IV: Further concepts and methods in survival analysis. British Journal of Cancer 89, 781-786. [free pdf]


Collett, D. (1991) Modelling survival data in medical research. Chapman & Hall, London.


Draper, N. & Smith, H. (1998). Applied regression analysis. 3rd edn. Wiley-Blackwell. 736 pp.


Eberhardt, L.L. (2002). A paradigm for population analysis of long-lived vertebrates. Ecology 83 (10), 2841-2854.[2841:APFPAO]2.0.CO;2


Fleming, T.F. & Lin, D.Y. (2000). Survival analysis in clinical trials: past developments and future directions. Biometrics 56 (4), 971-983.


Fox, G.A. (2001). Failure-time analysis: Studying times to events and rates at which events occur. pp 235-266. In: Scheiner, S.M. & Gurevitch, J. Design and Analysis of Ecological Experiments. Oxford University Press.


Friendly, M. & Kwan, E. (2009). Where's Waldo: Visualizing collinearity diagnostics. The American Statistician 63 (1), 53-65. [free pdf]


Grambsch P.M. & Therneau, T.M. (1994) Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81 (3), 515-526 . [free pdf]


Grömping, U. (2006). Relative Importance for Linear Regression in R: The Package relaimpo. Journal of Statistical Software 17 (1), 238-257. [free pdf]


Grömping, U. (2007). Estimators of relative importance in linear regression based on variance decomposition. The American Statistician 61 (2), 139-147.


Heagerty, P.J. & Zheng, Y. (2002). Survival model predictive accuracy and ROC curves Biometrics 61, 92-105. [free pdf]


Harrell, F.E. et al. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15 (4), 361-387.<361::AID-SIM168>3.0.CO;2-4 [free pdf]


Hilbe, J.M. (2009) . Logistic regression models. Chapman & Hall.


Hector, A. et al. (2010). Analysis of variance with unbalanced data: an update for ecology and evolution. Journal of Animal Ecology 79 (2), 308-316. [free pdf]


Henderson, H. V. &Velleman, P. F. (1981). Building multiple regression models interactively. Biometrics 37 (2), 391-411.


Hosmer, D.W. & Lemeshow, S. (2000). Applied logistic regression. 2nd Edn. Wiley, New York.


Hosmer, D.W. & Lemeshow, S. (2008). Applied survival analysis. Regression modelling of time to event data. 2nd Edn. Wiley-Interscience. 416 pp.


Ishiguro, M. et al. (1997). Bootstrapping log likelihood and EIC, an extension of AIC. Annals of Institute of Statistical Mathematics 49 (3), 411-434. [free pdf]


Johnson, J.W. & Lebreton, J.M. (2004). History and use of relative importance indices in organizational research. Organizational Research Methods 7 (3), 238-257.


Johnson, J.B. & Omland, K.S. (2004). Model selection in ecology and evolution. TRENDS in Ecology and Evolution 19 (2), 101-109. [free pdf]


King , J.E. (2003). Running a best-subsets logistic regression: An alternative to stepwise methods. Educational and Psychological Measurement 63 (3), 392-403.


Kleinbaum, D.G. & Klein, M. (2011). Survival Analysis: A Self-Learning Text (Statistics for Biology and Health). 3rd edn. Springer, New York. 636 pp. doi:


Lebreton, J.-D. et al. (1993). The statistical analysis of survival in animal populations. Trends in Ecology and Evolution 8 (3), 91-95.


Lee, J. & Chia, K.S. (1993) Estimation of prevalence rate ratios for cross sectional data: an example in occupational epidemiology (Letter). British Journal of Industrial Medicine 50, 861-864. [free pdf]


Lin D, et al. (1993). Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 80(3), 557-572. [free pdf]


Lindeman, R.H. et al. (1980). Introduction to Bivariate and Multivariate Analysis. Scott, Foresman, Glenview, IL.


Mac Nally, R. (2000). Regression and model-building in conservation biology, biogeography and ecology: The distinction between - and reconciliation of - 'predictive' and 'explanatory' models. Biodiversity and Conservation 9, 655-671. [free pdf]


Mac Nally, R. (2002). Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodiversity and Conservation 11, 1397-1401. [free pdf]


Machin, D. et al. (2006). Survival Analysis: A Practical Approach. 2nd Edn. Wiley-Blackwell. 278 pp.


McQuarrie, A.D.R. & Tsai, C-L. (1998). Regression and the Time Series Model Selection. World Scientific Publishing Co. 455 pp.


McCullagh & Nelder (1989). Generalized linear models. 2nd Edn. Chapman & Hall/CRC, London.


Mittlböck, M. & Schemper, M. (1996). Explained variation for logistic regression. Statistics in Medicine 15 (19), 1987-1997.<1987::AID-SIM318>3.0.CO;2-9 [free pdf]


Mittlböck, M & Schemper, M. (2002). Explained variation for logistic regression - Small sample adjustments, confidence intervals and predictive precision. Biometrical Journal 44 (3), 263-272.<263::AID-BIMJ263>3.0.CO;2-7 [free pdf]


Muenchow, G. (1986). Ecological use of failure time analysis. Ecology 67 (3), 246-250.


Nagelkerke, N.J.D. (1991). A note on general definition of the coefficient of determination. Biometrika 78 (3), 691-692.


Nemes, S. et al. (2009) . Bias in odds ratios by logistic regression modelling and sample size. BMC Medical Research Methodology 9: 56. [free]


Richards, S.A (2005). Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology 86 (10), 2805-2814. [free pdf]


Royston, P. et al. (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in Medicine 25 (1), 127-141. [free pdf]


Rousseeuw, P. J. (1991). Tutorial to robust statistics. Journal of Chemometrics 5 (1), 1-20. [free pdf]


Rousseeuw, P. J. & Leroy, A.M.(2003). Robust regression and outlier detection. Wiley-Blackwell. 360pp.


Rutherford, G.N. et al. (2007). Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes. Journal of Applied Ecology 44 (2), 414-424. [free pdf]


Schemper, M. et al. (1992). Cox analysis of survival data with non-proportional hazard functions. Journal of the Royal Statistical Society. Series D(The Statistician) 41 (4), 455-465.


Schemper, M. (1993). The relative importance of prognostic factors in studies of survival. Statistics in Medicine 12 (24), 2377-2382. [free pdf]


Schemper, M. & Henderson, R. (2000). Predictive accuracy and explained variation in Cox regression. Biometrics 56, 249-255. [free pdf]


Schemper, M. & Stare, J. (1996). Explained variation in survival analysis. Statistics in Medicine 15 (19), 1999-1012.<1999::AID-SIM353>3.0.CO;2-D [free pdf]


Slinker, B.K. & Glantz, S.A. (2008) Multiple linear regression: Accounting for multiple simultaneous determinants of a continuous dependent variable. Circulation 117, 1732-1737. [free pdf]


Steyerberg, E.W. et al. (1999) . Stepwise selection in small data sets: A simulation study of bias in logistic regression analysis. Journal of Clinical Epidemiology 52 (10), 935-942.


Tenhumberg, B. et al. (2001) Using Cox's Proportional Hazard Models to implement optimal strategies: An example from behavioural ecology. Mathematical and Computer Modelling 33 (6-7), 597-607. [free pdf]


Whittingham, M.J. et al. (2006). Why do we still use stepwise modelling in ecology and behaviour. Journal of Animal Ecology 75 (5), 1182-1189. [free pdf]


Xue, X. et al. (2007). Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure. Lifetime Data Analysis 13 (3), 333-350.


Zuur, A.F. et al. (2007). Analyzing Ecological Data. Springer, New York. 672 pp.