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Statistics and Data Science

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Cover of 'Statistics and Data Science'

Table of Contents

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    Book Overview
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    Chapter 1 Symbolic Formulae for Linear Mixed Models
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    Chapter 2 code::proof: Prepare for Most Weather Conditions
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    Chapter 3 Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
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    Chapter 4 Flexible Modelling via Multivariate Skew Distributions
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    Chapter 5 Estimating Occupancy and Fitting Models with the Two-Stage Approach
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    Chapter 6 Component Elimination Strategies to Fit Mixtures of Multiple Scale Distributions
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    Chapter 7 An Introduction to Approximate Bayesian Computation
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    Chapter 8 Truth, Proof, and Reproducibility: There’s No Counter-Attack for the Codeless
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    Chapter 9 On Adaptive Gauss-Hermite Quadrature for Estimation in GLMM’s
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    Chapter 10 Deep Learning with Periodic Features and Applications in Particle Physics
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    Chapter 11 Copula Modelling of Nurses’ Agitation-Sedation Rating of ICU Patients
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    Chapter 12 Predicting the Whole Distribution with Methods for Depth Data Analysis Demonstrated on a Colorectal Cancer Treatment Study
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    Chapter 13 Resilient and Deep Network for Internet of Things (IoT) Malware Detection
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    Chapter 14 Prediction of Neurological Deterioration of Patients with Mild Traumatic Brain Injury Using Machine Learning
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    Chapter 15 Spherical Data Handling and Analysis with R package rcosmo
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    Chapter 16 On the Parameter Estimation in the Schwartz-Smith’s Two-Factor Model
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    Chapter 17 Interval Estimators for Inequality Measures Using Grouped Data
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    Chapter 18 Exact Model Averaged Tail Area Confidence Intervals
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22 tweeters

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Title
Statistics and Data Science
Published by
Springer Singapore, January 2019
DOI 10.1007/978-981-15-1960-4
ISBNs
978-9-81-151959-8, 978-9-81-151960-4
Editors

Hien Nguyen

Twitter Demographics

The data shown below were collected from the profiles of 22 tweeters 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 54%
Researcher 4 31%
Student > Master 3 23%
Student > Bachelor 1 8%
Student > Postgraduate 1 8%
Other 0 0%
Readers by discipline Count As %
Computer Science 5 38%
Engineering 2 15%
Economics, Econometrics and Finance 1 8%
Immunology and Microbiology 1 8%
Mathematics 1 8%
Other 5 38%