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Advances in Statistical Models for Data Analysis

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Cover of 'Advances in Statistical Models for Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Using the dglars Package to Estimate a Sparse Generalized Linear Model
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    Chapter 2 A Depth Function for Geostatistical Functional Data
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    Chapter 3 Robust Clustering of EU Banking Data
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    Chapter 4 Sovereign Risk and Contagion Effects in the Eurozone: A Bayesian Stochastic Correlation Model
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    Chapter 5 Female Labour Force Participation and Selection Effect: Southern vs Eastern European Countries
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    Chapter 6 Asymptotics in Survey Sampling for High Entropy Sampling Designs
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    Chapter 7 A Note on the Use of Recursive Partitioning in Causal Inference
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    Chapter 8 Meta-Analysis of Poll Accuracy Measures: A Multilevel Approach
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    Chapter 9 Families of Parsimonious Finite Mixtures of Regression Models
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    Chapter 10 Quantile Regression for Clustering and Modeling Data
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    Chapter 11 Nonmetric MDS Consensus Community Detection
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    Chapter 12 The Performance of the Gradient-Like Influence Measure in Generalized Linear Mixed Models
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    Chapter 13 New Flexible Probability Distributions for Ranking Data
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    Chapter 14 Robust Estimation of Regime Switching Models
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    Chapter 15 Incremental Visualization of Categorical Data
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    Chapter 16 A New Proposal for Tree Model Selection and Visualization
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    Chapter 17 Object-Oriented Bayesian Network to Deal with Measurement Error in Household Surveys
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    Chapter 18 Comparing Fuzzy and Multidimensional Methods to Evaluate Well-Being in European Regions
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    Chapter 19 Cluster Analysis of Three-Way Atmospheric Data
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    Chapter 20 Asymmetric CLUster Analysis Based on SKEW-Symmetry: ACLUSKEW
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    Chapter 21 Parsimonious Generalized Linear Gaussian Cluster-Weighted Models
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    Chapter 22 New Perspectives for the MDC Index in Social Research Fields
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    Chapter 23 Clustering Methods for Ordinal Data: A Comparison Between Standard and New Approaches
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    Chapter 24 Novelty Detection with One-Class Support Vector Machines
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    Chapter 25 Using Discrete-Time Multistate Models to Analyze Students’ University Pathways
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Advances in Statistical Models for Data Analysis
Published by
Springer, Cham, September 2015
DOI 10.1007/978-3-319-17377-1
978-3-31-917376-4, 978-3-31-917377-1

Isabella Morlini, Tommaso Minerva, Maurizio Vichi

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 2%
Germany 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 35%
Student > Master 10 19%
Student > Bachelor 4 7%
Student > Doctoral Student 3 6%
Researcher 3 6%
Other 9 17%
Unknown 6 11%
Readers by discipline Count As %
Social Sciences 9 17%
Engineering 7 13%
Agricultural and Biological Sciences 6 11%
Computer Science 4 7%
Environmental Science 3 6%
Other 16 30%
Unknown 9 17%