Discussion of "Calibrated Bayes, for Statistics in General, and Missing Data in Particular" by R. J. A. Little

Discussion of "Calibrated Bayes, for Statistics in General, and Missing Data in Particular" by R. Little [arXiv:1108.1917]Comment: Published in at http://dx.doi.org/10.1214/10-STS318B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Sequential Regression Multiple Imputation for Incomplete Multivariate Data using Markov Chain Monte Carlo

This paper discusses the theoretical background to handling missing data in a multivariate context. Earlier methods for dealing with item non-response are reviewed, followed by an examination of some of the more modern methods and, in particular, multiple imputation. One such technique, known as sequential regression multivariate imputation, which employs a Markov chain Monte Carlo algorithm is described and implemented. It is demonstrated that distributional convergence is rapid and only a few imputations are necessary in order to produce accurate point estimates and preserve multivariate relationships, whilst adequately accounting for the uncertainty introduced by the imputation procedure. It is further shown that lower fractions of missing data and the inclusion of relevant covariates in the imputation model are desirable in terms of bias reduction.Missing data; Item non-response; Missingness mechanism; Imputation; Regression; Markov chain Monte Carlo.

DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout

The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20\%-50\% of the inputs are not available

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Towards a Puviani’s Fiscal Illusion Index

This paper presents an index of Fiscal Illusion for 68 democratic countries from 1960 to 2006. The theory of Fiscal Illusion studied relates to a wrong perception of government budget aggregates from the voters’ and taxpayers’ perspectives. In the construction of the index, methodological issues were carefully taken into account. The results obtained reveal that Fiscal Illusion varies greatly around the world. Countries such as Mali, Pakistan, Russia, and Sri Lanka have the highest average values over the time period considered, while Austria, Luxembourg, Netherlands, and New Zealand have the lowest. Regarding the time dimension, between 1980 and 1995 there was a significant decrease in the average value of the index across countries, suggesting a reduction in the adoption of Fiscal Illusion practices during this period. After 1995, the index remained stable in most countries.Fiscal Illusion; Indexes/Indicators; Democracy.

Towards a Fiscal Illusion Index

This paper presents an index of Fiscal Illusion for 68 democratic countries from 1960 to 2006. The studied Fiscal Illusion is the one related to a wrong perception of the budget aggregates according to the voters and taxpayers’ perspectives. In the construction of the index, methodological issues were carefully taken into account. The results obtained reveal that fiscal illusion varies greatly around the world. Countries such as Mali, Pakistan, Russia and Sri Lanka have the highest average values over the time period considered; while Austria, Luxembourg, Netherlands and New Zealand have the lowest. Regarding the time dimension, between 1980 and 1995 there was a significant decrease in the average value of the index across countries, suggesting a reduction in the adoption of fiscal illusion measures during this period. After 1995, the index remained stable in most of the countries.Fiscal Illusion; Indexes/Indicators; Democracy