Statistical Analysis with Missing Data

Basics Introduction The problem of missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Multiple imputation in a nutshell Goal of the book What the book does not cover Structure of the book Exercises Multiple imputation Historic overview Incomplete data concepts Why and when multiple imputation works Statistical intervals and tests Evaluation criteria When to use multiple imputation How many imputations? Exercises Univariate missing data How to generate multiple imputations Imputation under the normal linear normal Imputation under non-normal distributions Predictive mean matching Categorical data Other data types Classification and regression trees Multilevel data Non-ignorable methods Exercises Multivariate missing data Missing data pattern Issues in multivariate imputation Monotone data imputation Joint Modeling Fully Conditional Specification FCS and JM Conclusion Exercises Imputation in practice Overview of modeling choices Ignorable or non-ignorable? Model form and predictors Derived variables Algorithmic options Diagnostics Conclusion Exercises Analysis of imputed data What to do with the imputed data? Parameter pooling Statistical tests for multiple imputation Stepwise model selection Conclusion Exercises Case studies Measurement issues Too many columns Sensitivity analysis Correct prevalence estimates from self-reported data Enhancing comparability Exercises Selection issues Correcting for selective drop-out Correcting for non-response Exercises Longitudinal data Long and wide format SE Fireworks Disaster Study Time raster imputation Conclusion Exercises Extensions Conclusion Some dangers, some do's and some don'ts Reporting Other applications Future developments Exercises Appendices: Software R S-Plus Stata SAS SPSS Other software References Author Index Subject Index

Flexible Imputation of Missing Data

Fifty years have passed since the publication of the first regression tree algorithm. New techniques have added capabilities that far surpass those of the early methods. Modern classification trees can partition the data with linear splits on subsets of variables and fit nearest neighbor, kernel density, and other models in the partitions. Regression trees can fit almost every kind of traditional statistical model, including least-squares, quantile, logistic, Poisson, and proportional hazards models, as well as models for longitudinal and multiresponse data. Greater availability and affordability of software (much of which is free) have played a significant role in helping the techniques gain acceptance and popularity in the broader scientific community. This article surveys the developments and briefly reviews the key ideas behind some of the major algorithms.

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Fifty Years of Classification and Regression Trees

National Institute of Environmental Health Sciences 魅せられる"生物学の面白さ"

Multiple imputation is particularly well suited to deal with missing data in large epidemiologic studies, because typically these studies support a wide range of analyses by many data users. Some of these analyses may involve complex modeling, including interactions and nonlinear relations. Identifying such relations and encoding them in imputation models, for example, in the conditional regressions for multiple imputation via chained equations, can be daunting tasks with large numbers of categorical and continuous variables. The authors present a nonparametric approach for implementing multiple imputation via chained equations by using sequential regression trees as the conditional models. This has the potential to capture complex relations with minimal tuning by the data imputer. Using simulations, the authors demonstrate that the method can result in more plausible imputations, and hence more reliable inferences, in complex settings than the naive application of standard sequential regression imputation techniques. They apply the approach to impute missing values in data on adverse birth outcomes with more than 100 clinical and survey variables. They evaluate the imputations using posterior predictive checks with several epidemiologic analyses of interest.

Multiple Imputation for Missing Data via Sequential Regression Trees

Aim of this paper is to investigate the extent of the impact of the Airbnb phenomenon on tourism indicators in general and, in particular, on sustainable tourism indicators (STI). We conjecture tha...

The impact of Airbnb on hidden and sustainable tourism: the case of Italy

In this paper we introduce a methodology based on a combination of classification/prediction procedures derived from different approaches. In particular, starting from a general definition of a classification/prediction model named Generalized Additive Multi-Model (GAM-M) we will demonstrate how it is possible to obtain different types of statistical models based on parametric, semiparametric and nonparametric methods. In our methodology the estimation procedure is based on a variant of the backfitting algorithm used for Generalized Additive Models (GAM). The benchmarking of our methodology will be shown and the results will be compared with those derived from the applications of GAM and Tree procedures.

Generalized Additive Multi-Model for Classification and Prediction

We propose a Machine Learning approach for Image Validation (MaLIV) to rank the performances of two or more outputs obtained from different gray-level thresholding image segmentation algorithms. MaLIV utilizes machine learning classifiers to rank automatically the outputs of different segmentation algorithms accounting for both the computational complexity of the validation experiment and for the robustness of its results. The proposed method resorts to subsampling to find Fisher consistent estimates of validity measures obtained from a sample of pixels of extremely-reduced size. To this purpose, subsampling is combined with three alternative approaches: learning curves, asymptotic regression and convergence in probability. Results of experiments involving the validation of five images segmented through thirteen different algorithms are presented.

Consistent validation of gray-level thresholding image segmentation algorithms based on machine learning classifiers

Tree Harvest: Methods, Software and Some Applications

Predicting popularity from social media has been explored about a decade. As far as the number of social media users is soaring, understanding the relationship between popularity and social media is really beneficial because it can be mapped to the real popularity of an entity. The popularity in social media, for instance in Twitter, is interpreted by drawing a relationship between a social media account and its followers. Therefore, in this paper, to understand the popularity of candidates of the US election 2016 in social media, we verify this association in Twitter by analyzing the candidates’ tweets. More specifically, our aim is to assess if candidates put efforts to improve their style of tweeting over time to be more favorable to their followers. We show that Mr. Trump could wisely exploit Twitter to attract more people by tweeting in a well-organized and desirable manner and that tweeting style has increased his popularity in social media.

Claudio Conversano

Papers

The impact of Airbnb on hidden and sustainable tourism: the case of Italy

Generalized Additive Multi-Model for Classification and Prediction

Consistent validation of gray-level thresholding image segmentation algorithms based on machine learning classifiers

Tree Harvest: Methods, Software and Some Applications

Investigating the Relationship Between Tweeting Style and Popularity: The Case of US Presidential Election 2016