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Unpacking the MPI : A Decomposition Approach of Changes in Multidimensional Poverty Headcounts.
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Year: 2015 Publisher: Washington, D.C. : The World Bank,

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Multidimensional measures of poverty have become standard as complementary indicators of poverty in many countries. Multidimensional poverty calculations typically comprise three indices: the multidimensional headcount, the average deprivation share among the poor, and the adjusted headcount ratio. While several decomposition methodologies are available for the last index, less attention has been paid to decomposing the multidimensional headcount, despite the attention it receives from policy makers. This paper proposes an application of existing methodologies that decompose welfare aggregates-based on counterfactual simulations-to break up the changes of the multidimensional poverty headcount into the variation attributed to each of its dimensions. This paper examines the potential issues of using counterfactual simulations in this framework, proposes approaches to assess these issues in real applications, and suggests a methodology based on rank preservation within strata, which performs positively in simulations. The methodology is applied in the context of the recent reduction of multidimensional poverty in Colombia, finding that the dimensions associated with education and health are the main drivers behind the poverty decline.


Book
Unpacking the MPI : A Decomposition Approach of Changes in Multidimensional Poverty Headcounts.
Authors: --- --- ---
Year: 2015 Publisher: Washington, D.C. : The World Bank,

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Abstract

Multidimensional measures of poverty have become standard as complementary indicators of poverty in many countries. Multidimensional poverty calculations typically comprise three indices: the multidimensional headcount, the average deprivation share among the poor, and the adjusted headcount ratio. While several decomposition methodologies are available for the last index, less attention has been paid to decomposing the multidimensional headcount, despite the attention it receives from policy makers. This paper proposes an application of existing methodologies that decompose welfare aggregates-based on counterfactual simulations-to break up the changes of the multidimensional poverty headcount into the variation attributed to each of its dimensions. This paper examines the potential issues of using counterfactual simulations in this framework, proposes approaches to assess these issues in real applications, and suggests a methodology based on rank preservation within strata, which performs positively in simulations. The methodology is applied in the context of the recent reduction of multidimensional poverty in Colombia, finding that the dimensions associated with education and health are the main drivers behind the poverty decline.

Introduction to mathematical programming : Applications and algorithms.
Author:
ISBN: 0534230466 9780534230463 Year: 1995 Publisher: Belmont : Duxbury Press,


Book
Analysis of Sensory Properties in Foods
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ISBN: 3039214349 3039214330 Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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The sensory properties of foods are the most important reason people eat the foods they eat. What those properties are and how we best measure those properties are critical to understanding food and eating behavior. Appearance, flavor, texture, and even the sounds of food can impart a desire to eat or cause us to dismiss the food as unappetizing, stale, or even inappropriate from a cultural standpoint. This Special Issue focuses on how sensory properties are measured, the specific sensory properties of various foods, and consumer behavior related to which properties might be most important in certain situations and how consumers use sensory attributes to make decisions about what they will eat. This Special Issue contains both research papers and review articles.


Book
Assessment of Energy–Environment–Economy Interrelations
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ISBN: 3039288105 3039288091 Year: 2020 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Energy consumption and economic growth have been of great interest to researchers and policy-makers. Knowing the actual causal relationship between energy and the economy with respect to environmental degradation has important implications for modeling environmental and growth policies. The eleven chapters included herein aim to help researchers, academicians, and especially decision-makers to understand relevant issues and adopt appropriate methods to tackle and solve relevant environmental problems. Various methods from different disciplines are proposed and applied to various environmental and energy issues.


Book
Energy Data Analytics for Smart Meter Data
Authors: ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Keywords

Technology: general issues --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks


Book
Energy Data Analytics for Smart Meter Data
Authors: ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Keywords

Technology: general issues --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- n/a


Book
Advances in Game Theory. (AM-52), Volume 52
Authors: --- ---
ISBN: 140088201X Year: 2016 Publisher: Princeton, NJ : Princeton University Press,

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The description for this book, Advances in Game Theory. (AM-52), Volume 52, will be forthcoming.

Keywords

Game theory. --- Almost surely. --- Automorphism. --- Axiom. --- Basis (linear algebra). --- Bayesian probability. --- Big O notation. --- Bounded set (topological vector space). --- Calculation. --- Cartesian product. --- Characteristic function (probability theory). --- Complete theory. --- Conditional probability distribution. --- Continuous function (set theory). --- Continuum hypothesis. --- Cooperative game. --- Coset. --- Counterexample. --- Cumulative distribution function. --- Decision rule. --- Decision-making. --- Determinacy. --- Diagram (category theory). --- Differential game. --- Distribution function. --- Dyadic rational. --- Equation solving. --- Equation. --- Equilibrium point. --- Estimation. --- Existence theorem. --- Family of sets. --- Foundations of mathematics. --- Function (mathematics). --- Fundamental theorem. --- Game show. --- Hamilton–Jacobi equation. --- Harmonic function. --- Independence (probability theory). --- Inequality (mathematics). --- Infimum and supremum. --- Initial value problem. --- Invertible matrix. --- Jacobian matrix and determinant. --- Joint probability distribution. --- Linear inequality. --- Linear map. --- Linear programming. --- Lipschitz continuity. --- Markov chain. --- Markov process. --- Markov property. --- Mathematical analysis. --- Mathematical economics. --- Mathematical induction. --- Mathematical optimization. --- Matrix (mathematics). --- Minimax theorem. --- Minor (linear algebra). --- Mutual exclusivity. --- N-vector. --- Open set. --- Outcome (probability). --- Parity (mathematics). --- Partially ordered set. --- Payment. --- Permutation. --- Preference (economics). --- Prime number. --- Primitive root modulo n. --- Probability distribution function. --- Probability distribution. --- Probability measure. --- Probability. --- Quantifier (logic). --- Quantity. --- Random variable. --- Randomization. --- Ranking (information retrieval). --- Representation theory. --- Sample space. --- Scientific notation. --- Search game. --- Set (mathematics). --- Shapley value. --- Simultaneous equations. --- Skew-symmetric matrix. --- Solution concept. --- Special case. --- Strategy (game theory). --- Subset. --- Summation. --- Superadditivity. --- Sylow theorems. --- Theorem. --- Theory of Games and Economic Behavior. --- Theory. --- Topology. --- Utility. --- Variable (mathematics). --- Weighted arithmetic mean.


Book
Energy Data Analytics for Smart Meter Data
Authors: ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Keywords

smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- n/a

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