Prediction in several conventional contexts

We review predictive techniques from several traditional branches of statistics. Starting with prediction based on the normal model and on the empirical distribution function, we proceed to techniques for various forms of regression and classification. Then, we turn to time series, longitudinal data, and survival analysis. Our focus throughout is on the mechanics of prediction more than on the properties of predictors.

[1]  L. Mark Berliner,et al.  Bayesian Nonparametric Survival Analysis , 1988 .

[2]  Bertrand Clarke,et al.  Desiderata for a Predictive Theory of Statistics , 2010 .

[3]  W. Dunsmuir,et al.  Least Absolute Deviation Estimation for Regression with ARMA Errors , 1997 .

[4]  G. Reinsel,et al.  Models for Longitudinal Data with Random Effects and AR(1) Errors , 1989 .

[5]  R. Cook,et al.  Assessing influence on predictions from generalized linear models , 1990 .

[6]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[7]  Keming Yu,et al.  Bayesian quantile regression , 2001 .

[8]  C. Smith.  Diagnostic tests (1) – sensitivity and specificity , 2012, Phlebology.

[9]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[10]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[11]  Steven de Rooij,et al.  Catching Up Faster by Switching Sooner: A Prequential Solution to the AIC-BIC Dilemma , 2008, ArXiv.

[12]  Seymour Geisser,et al.  8. Predictive Inference: An Introduction , 1995 .

[13]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[14]  Nicolai Meinshausen,et al.  Quantile Regression Forests , 2006, J. Mach. Learn. Res..

[15]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[16]  A. Zellner,et al.  Bayesian analysis of the federal reserve- MIT-Penn model's almon lag consumption function , 1973 .

[17]  Jiming Jiang.  Linear and Generalized Linear Mixed Models and Their Applications , 2007 .

[18]  R. Gill.  Large Sample Behaviour of the Product-Limit Estimator on the Whole Line , 1983 .

[19]  Anne Philippe,et al.  Bayesian analysis of autoregressive moving average processes with unknown orders , 2006, Comput. Stat. Data Anal..

[20]  S. Chib,et al.  Bayes inference in regression models with ARMA (p, q) errors , 1994 .

[21]  A. Földes,et al.  STRONG UNIFORM CONSISTENCY FOR NONPARAMETRIC SURVIVAL CURVE ESTIMATORS FROM RANDOMLY CENSORED DATA , 1981 .

[22]  M. May.  Bayesian Survival Analysis. , 2002 .

[23]  Robert E. Weiss,et al.  An Analysis of Paediatric Cd4 Counts for Acquired Immune Deficiency Syndrome Using Flexible Random Curves , 1996 .

[24]  M. Pagano,et al.  Survival analysis. , 1996, Nutrition.

[25]  M N Chang,et al.  Interim analysis for randomized clinical trials: simulating the predictive distribution of the log-rank test statistic. , 1994, Biometrics.

[26]  Judith D. Goldberg,et al.  Applied Survival Analysis , 1999, Technometrics.

[27]  Steffen Lauritzen,et al.  PROPER LOCAL SCORING RULES ON DISCRETE SAMPLE SPACES , 2011, 1104.2224.

[28]  J. Aitchison.  Goodness of prediction fit , 1975 .

[29]  M. Draper,et al.  On the Relationship Between Model Uncertainty and Inferential/predictive Uncertainty , 1997 .

[30]  Colin O. Wu,et al.  Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves , 2001, Biometrics.

[31]  N. Cook.  Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. , 2008, Clinical chemistry.

[32]  James R. Kenyon,et al.  Statistical Methods for the Analysis of Repeated Measurements , 2003, Technometrics.

[33]  V. A. Samaranayake,et al.  Prediction Bounds for the Weibull Distribution , 2004 .

[34]  David W. Hosmer,et al.  Applied Survival Analysis: Regression Modeling of Time-to-Event Data , 2008 .

[35]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[36]  D. Raghavarao,et al.  Growth Curve Models , 2014 .

[37]  Robert Kohn,et al.  ROBUST BAYESIAN ESTIMATION OF AUTOREGRESSIVE‐‐MOVING‐AVERAGE MODELS , 1997 .

[38]  Catherine A. Sugar,et al.  Principal component models for sparse functional data , 1999 .

[39]  T J Cole,et al.  Smoothing reference centile curves: the LMS method and penalized likelihood. , 1992, Statistics in medicine.

[40]  James W. Taylor.  A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns , 2000 .

[41]  G. Robinson.  That BLUP is a Good Thing: The Estimation of Random Effects , 1991 .

[42]  J. Monahan.  Fully Bayesian analysis of ARMA time series models , 1983 .

[43]  Margaret T May,et al.  Statistical Methods for the Analysis of Repeated Measurements.Charles S Davis. Heidelberg: Springer Verlag, 2002, pp. 415, £59.50 (HB) ISBN: 0-387-95370-1. , 2003 .

[44]  Hao Zhang.  On Estimation and Prediction for Spatial Generalized Linear Mixed Models , 2002, Biometrics.

[45]  Leon G. Higley,et al.  Forensic Entomology: An Introduction , 2009 .

[46]  David Collett.  Modelling Survival Data in Medical Research , 1994 .

[47]  J. G. De Gooijer,et al.  Methods for Determining the Order of an Autoregressive-Moving Average Process: A , 1985 .

[48]  R. Sakia.  The Box-Cox transformation technique: a review , 1992 .

[49]  P. Diggle.  Analysis of Longitudinal Data , 1995 .

[50]  Tom Fearn,et al.  Chemometric Space: Sensitivity and specificity , 2009 .

[51]  Yuhong Yang.  Can the Strengths of AIC and BIC Be Shared , 2005 .

[52]  Geert Molenberghs,et al.  Linear Mixed Models for Longitudinal Data (Paperback Edition) , 2009 .

[53]  J. Booth,et al.  Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm , 1999 .

[54]  J. Kalbfleisch,et al.  Marginal likelihoods based on Cox's regression and life model , 1973 .

[55]  D. Bates,et al.  Nonlinear mixed effects models for repeated measures data. , 1990, Biometrics.

[56]  Cécile Proust-Lima,et al.  Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. , 2009, Biostatistics.

[57]  J. Booth,et al.  Standard Errors of Prediction in Generalized Linear Mixed Models , 1998 .

[58]  P. Qiu.  The Statistical Evaluation of Medical Tests for Classification and Prediction , 2005 .

[59]  W. Feller.  On the Kolmogorov–Smirnov Limit Theorems for Empirical Distributions , 1948 .

[60]  G. Molenberghs.  Applied Longitudinal Analysis , 2005 .

[61]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[62]  P. Diggle.  Applied Spatial Statistics for Public Health Data , 2005 .

[63]  R. Reiss.  Approximate Distributions of Order Statistics , 1989 .

[64]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[65]  Michel Lejeune,et al.  A Simple Predictive Density Function , 1982 .

[66]  E. Hannan.  The Estimation of the Order of an ARMA Process , 1980 .

[67]  A. Brix.  Bayesian Data Analysis, 2nd edn , 2005 .

[68]  Donald E. Myers,et al.  Linear and Generalized Linear Mixed Models and Their Applications , 2008, Technometrics.

[69]  Ana Ivelisse Avilés,et al.  Linear Mixed Models for Longitudinal Data , 2001, Technometrics.

[70]  M Jones,et al.  Accuracy of point predictions in survival analysis , 2001, Statistics in medicine.

[71]  A. Zellner,et al.  Analysis of Distributed Lag Models with Application to Consumption Function Estimation , 1970 .

[72]  C. Radhakrishna Rao,et al.  Prediction of Future Observations in Growth Curve Models , 1987 .

[73]  P. McCullagh,et al.  Growth curve models , 2013 .

[74]  N. Breslow,et al.  A Large Sample Study of the Life Table and Product Limit Estimates Under Random Censorship , 1974 .

[75]  Bertrand Clarke,et al.  Principles and Theory for Data Mining and Machine Learning , 2009 .

[76]  A. Dawid,et al.  Prequential probability: principles and properties , 1999 .

[77]  Philip Hans Franses.  Testing for residual autocorrelation in growth curve models , 2002 .

[78]  R. Butler.  Predictive Likelihood Inference with Applications , 1986 .

[79]  Purushottam W. Laud,et al.  Predictive Variable Selection in Generalized Linear Models , 2002 .

[80]  Rupert G. Miller,et al.  Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.

[81]  D. Collet.  Modelling Survival Data in Medical Research , 2004 .

[82]  Marie Davidian,et al.  Nonlinear models for repeated measurement data: An overview and update , 2003 .

[83]  C. Link,et al.  Confidence intervals for the survival function using Cox's proportional-hazard model with covariates. , 1984, Biometrics.

[84]  N Keiding,et al.  Individual survival time prediction using statistical models , 2005, Journal of Medical Ethics.

[85]  M. Lawera.  Predictive inference : an introduction , 1995 .

[86]  Jorma Rissanen,et al.  Fisher information and stochastic complexity , 1996, IEEE Trans. Inf. Theory.

[87]  Bertrand Clarke,et al.  Improvement over bayes prediction in small samples in the presence of model uncertainty , 2004 .

[88]  S. Ghosal.  Semiparametric Accelerated Failure Time Models for Censored Data , 2006 .

[89]  Christian P. Robert,et al.  Bayesian Ideas and Data Analysis , 2012 .

[90]  Jeff Harrison,et al.  Applied Bayesian Forecasting and Time Series Analysis , 1994 .

[91]  Richard E. Chandler,et al.  Statistical Methods for Trend Detection and Analysis in the Environmental Sciences , 2011 .

[92]  J. Galbraith,et al.  Estimation of a linear regression model with stationary ARMA(p, q) errors , 1991 .

[93]  Adeline Samson,et al.  Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects. , 2008, Biostatistics.

[94]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[95]  W. Johnson,et al.  Predictive influence in the accelerated failure time model. , 2002, Biostatistics.