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New Developments in Statistical Information Theory Based on Entropy and Divergence Measures

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ISBN: 9783038979364 / 9783038979371 Year: Pages: 344 DOI: 10.3390/books978-3-03897-937-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Social Sciences --- Sociology --- Statistics
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.

Keywords

sparse --- robust --- divergence --- MM algorithm --- Bregman divergence --- generalized linear model --- local-polynomial regression --- model check --- nonparametric test --- quasi-likelihood --- semiparametric model --- Wald statistic --- composite likelihood --- maximum composite likelihood estimator --- Wald test statistic --- composite minimum density power divergence estimator --- Wald-type test statistics --- Bregman divergence --- general linear model --- hypothesis testing --- influence function --- robust --- Wald-type test --- log-linear models --- ordinal classification variables --- association models --- correlation models --- minimum penalized ?-divergence estimator --- consistency --- asymptotic normality --- goodness-of-fit --- bootstrap distribution estimator --- thematic quality assessment --- relative entropy --- logarithmic super divergence --- robustness --- minimum divergence inference --- generalized renyi entropy --- minimum divergence methods --- robustness --- single index model --- model assessment --- statistical distance --- non-quadratic distance --- total variation --- mixture index of fit --- Kullback-Leibler distance --- divergence measure --- ?-divergence --- relative error estimation --- robust estimation --- information geometry --- centroid --- Bregman information --- Hölder divergence --- indoor localization --- robustness --- efficiency --- Bayesian nonparametric --- Bayesian semi-parametric --- asymptotic property --- minimum disparity methods --- Hellinger distance --- Berstein von Mises theorem --- measurement errors --- robust testing --- two-sample test --- misspecified hypothesis and alternative --- 2-alternating capacities --- composite hypotheses --- corrupted data --- least-favorable hypotheses --- Neyman Pearson test --- divergence based testing --- Chernoff Stein lemma --- compressed data --- Hellinger distance --- representation formula --- iterated limits --- influence function --- consistency --- asymptotic normality --- location-scale family --- n/a

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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ISBN: 9783039212156 / 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

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MDPI - Multidisciplinary Digital Publishing Institute (2)


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CC by-nc-nd (2)


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eng (2)


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2019 (2)