InfluentialPoints.com
Biology, images, analysis, design...
Use/Abuse Stat.Book Beginners Stats & R
"It has long been an axiom of mine that the little things are infinitely the most important" (Sherlock Holmes)

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. http://dx.doi.org/10.1590/S0102-311X2008001600010 http://www.scielo.br/pdf/csp/v24s4/10.pdf [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. http://dx.doi.org/10.1016/j.jclinepi.2004.04.003 http://uncwddas.googlecode.com/files/article2.pdf [free pdf]

 

Begg, M.D. & Lagakos, S. (1990). On the consequences of model misspecification in logistic regression. Environmental Health Perspectives 87, 69-75. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1567834/pdf/envhper00420-0070.pdf [free pdf]

 

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

 

Bender, R. & Grouven, U. (1996). Logistic regression models used in medical research are poorly presented. (Letter) BMJ 313, 628 (7 September). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2352066/pdf/bmj00558-0064a.pdf [free pdf]

 

Beyene, J. et al. (2005). Determining relative importance of variables in developing and validating predictive models. BMC Medical Research Methodology 9: 64. http://dx.doi.org/10.1186/1471-2288-9-6 http://www.biomedcentral.com/content/pdf/1471-2288-9-64.pdf [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. http://www.jstor.org/stable/2290212

 

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. http://dx.doi.org/10.1111/j.1365-2028.2008.01014.x http://www.rbsd.de/PDF/npodds.pdf [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. http://dx.doi.org/10.1016/j.jclinepi.2007.03.002

 

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. http://dx.doi.org/10.1038/sj.bjc.6601119 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2394368/pdf/89-6601119a.pdf [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. http://dx.doi.org/10.1038/sj.bjc.6601120 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2376927/pdf/89-6601120a.pdf [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. http://www.iarc.fr/en/publications/pdfs-online/stat/sp32/SP32_vol1-7.pdf [free pdf]

 

Bring, J. et al. (1994). How to standardize regression coefficients. The American Statistician 48 (3), 209-213. http://www.jstor.org/pss/2684719

 

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. http://www.sma.org.sg/smj/4506/4506bs1.pdf [free pdf]

 

Chevan, A. & Sutherland, M. (1991). Hierarchical partitioning. The American Statistician 45 (2), 90-96. http://www.jstor.org/stable/2684366

 

Clark, T.G. et al. (2003a). Survival Analysis Part I: Basic concepts and first analyses British Journal of Cancer 89, 232 - 238. http://dx.doi.org/10.1038/sj.bjc.6601118 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2394262/pdf/89-6601118a.pdf [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. http://dx.doi.org/10.1038/sj.bjc.6601117 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2394469/pdf/89-6601117a.pdf [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. http://dx.doi.org/10.1890/0012-9658(2002)083[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. http://dx.doi.org/10.1111/j.0006-341X.2000.0971.x

 

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. http://dx.doi.org/10.1198/tast.2009.0012 http://datavis.ca/papers/viscollin-web.pdf [free pdf]

 

Grambsch P.M. & Therneau, T.M. (1994) Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81 (3), 515-526 . http://dx.doi.org/10.1093/biomet/81.3.515 http://ww.escarela.com/archivo/anahuac/03o/coxdiag.pdf [free pdf]

 

Grömping, U. (2006). Relative Importance for Linear Regression in R: The Package relaimpo. Journal of Statistical Software 17 (1), 238-257. http://www.jstatsoft.org/v17/i01/paper [free pdf]

 

Grömping, U. (2007). Estimators of relative importance in linear regression based on variance decomposition. The American Statistician 61 (2), 139-147. http://dx.doi.org/10.1198/000313007X188252

 

Heagerty, P.J. & Zheng, Y. (2002). Survival model predictive accuracy and ROC curves Biometrics 61, 92-105. http://dx.doi.org/10.1111/j.0006-341X.2005.030814.x http://staff.pubhealth.ku.dk/~tag/Teaching/share/material/heagerty-zheng-roc-survival.pdf [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. http://dx.doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 http://www.unt.edu/rss/class/Jon/MiscDocs/Harrell_1996.pdf [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. http://dx.doi.org/10.1111/j.1365-2656.2009.01634.x http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2009.01634.x/pdf [free pdf]

 

Henderson, H. V. &Velleman, P. F. (1981). Building multiple regression models interactively. Biometrics 37 (2), 391-411. http://www.jstor.org/stable/2530428

 

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. http://dx.doi.org/10.1023/A:1003158526504 http://miwin4.ism.ac.jp/~ishiguro/Profiss/lecture.dir/EICPDF.PDF [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. http://dx.doi.org/10.1177/1094428104266510

 

Johnson, J.B. & Omland, K.S. (2004). Model selection in ecology and evolution. TRENDS in Ecology and Evolution 19 (2), 101-109. http://dx.doi.org/10.1016/j.tree.2003.10.013 http://faculty.washington.edu/skalski/classes/QERM597/papers/Johnson%20and%20Omland.pdf [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. http://dx.doi.org/10.1177/0013164403063003003

 

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: http://dx.doi.org/10.1007/0-387-29150-4

 

Lebreton, J.-D. et al. (1993). The statistical analysis of survival in animal populations. Trends in Ecology and Evolution 8 (3), 91-95. http://dx.doi.org/10.1016/0169-5347(93)90058-W

 

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. http://dx.doi.org/10.1136/oem.50.9.861 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1061320/pdf/brjindmed00009-0093.pdf [free pdf]

 

Lin D, et al. (1993). Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 80(3), 557-572. http://dx.doi.org/10.1093/biomet/80.3.557 http://www.bios.unc.edu/~lin/publications/1993/LinWeiYing93.pdf [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. http://dx.doi.org/10.1023/A:1008985925162 http://corpolac.corpoica.org.co/SitioWeb/Documento/JatrophaContrataciones/MODELOSPREDICTIVOS-JATROPHA.pdf [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. http://dx.doi.org/10.1023/A:1016250716679 http://www.geog.ubc.ca/courses/geob479/notes/spatial_analysis/multiple_regression_in_CB.pdf [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. http://dx.doi.org/10.1002/(SICI)1097-0258(19961015)15:19<1987::AID-SIM318>3.0.CO;2-9 http://www.meduniwien.ac.at/imc/biometrie/publikationen/Separata/Mittlboeck_Schemper_1996_Statistics%20in%20Medicine.pdf [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. http://dx.doi.org/10.1002/1521-4036(200204)44:3<263::AID-BIMJ263>3.0.CO;2-7 http://www.meduniwien.ac.at/msi/biometrie/publikationen/Separata/Mittlboeck_Schemper_2002_Biometrical_Journal.pdf [free pdf]

 

Muenchow, G. (1986). Ecological use of failure time analysis. Ecology 67 (3), 246-250. http://dx.doi.org/10.2307/1938524

 

Nagelkerke, N.J.D. (1991). A note on general definition of the coefficient of determination. Biometrika 78 (3), 691-692. http://dx.doi.org/10.1093/biomet/78.3.691

 

Nemes, S. et al. (2009) . Bias in odds ratios by logistic regression modelling and sample size. BMC Medical Research Methodology 9: 56. http://dx.doi.org/10.1186/1471-2288-9-56 http://www.biomedcentral.com/content/pdf/1471-2288-9-56.pdf [free]

 

Richards, S.A (2005). Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology 86 (10), 2805-2814. http://dx.doi.org/10.1890/05-0074 http://www.uvm.edu/~bbeckage/Teaching/DataAnalysis/AssignedPapers/InformationTheory.Ecology_2005.pdf [free pdf]

 

Royston, P. et al. (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in Medicine 25 (1), 127-141. http://dx.doi.org/10.1002/sim.2331 http://www-psychology.concordia.ca/fac/kline/601/royston.pdf [free pdf]

 

Rousseeuw, P. J. (1991). Tutorial to robust statistics. Journal of Chemometrics 5 (1), 1-20. http://dx.doi.org/10.1002/cem.1180050103 ftp://ftp.win.ua.ac.be/pub/preprints/91/Tutrob91.pdf [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. http://dx.doi.org/10.1111/j.1365-2664.2007.01281.x http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2664.2007.01281.x/pdf [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. http://www.jstor.org/stable/2349009

 

Schemper, M. (1993). The relative importance of prognostic factors in studies of survival. Statistics in Medicine 12 (24), 2377-2382. http://dx.doi.org/10.1002/sim.4780122413 http://www.meduniwien.ac.at/msi/biometrie/publikationen/Separata/Schemper_1993_Statistics%20in%20Medicine.pdf [free pdf]

 

Schemper, M. & Henderson, R. (2000). Predictive accuracy and explained variation in Cox regression. Biometrics 56, 249-255. http://dx.doi.org/10.1111/j.0006-341X.2000.00249.x http://www.meduniwien.ac.at/msi/biometrie/publikationen/Separata/Schemper_Henderson_2000_Biometrics.pdf [free pdf]

 

Schemper, M. & Stare, J. (1996). Explained variation in survival analysis. Statistics in Medicine 15 (19), 1999-1012. http://dx.doi.org/10.1002/(SICI)1097-0258(19961015)15:19<1999::AID-SIM353>3.0.CO;2-D http://www.meduniwien.ac.at/imc/biometrie/publikationen/Separata/Schemper_Stare_1996_Statistics%20in%20Medicine.pdf [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. http://dx.doi.org/10.1161/CIRCULATIONAHA.106.654376 http://circ.ahajournals.org/cgi/reprint/117/13/1732.pdf [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. http://www.jclinepi.com/article/S0895-4356(99)00103-1/abstract

 

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. http://dx.doi.org/10.1016/S0895-7177(00)00264-8 http://espace.library.uq.edu.au/eserv.php?pid=UQ:10184&dsID=poss4.pdf [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. http://dx.doi.org/10.1111/j.1365-2656.2006.01141.x http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2006.01141.x/pdf [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. http://dx.doi.org/10.1007/s10985-007-9045-1

 

Zuur, A.F. et al. (2007). Analyzing Ecological Data. Springer, New York. 672 pp. http://dx.doi.org/10.1007/978-0-387-45972-1