Juan Aparicio

About Juan Aparicio

Juan Aparicio, With an exceptional h-index of 32 and a recent h-index of 29 (since 2020), a distinguished researcher at Universidad Miguel Hernández de Elche, specializes in the field of Efficiency, Productivity, Data Envelopment Analysis, Machine learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Estimating production functions through additive models based on regression splines

The defence economy: an assessment of productivity change in NATO countries

Evaluating different methods for ranking inputs in the context of the performance assessment of decision making units: A machine learning approach

Benchmarking performance through efficiency analysis trees: Improvement strategies for colombian higher education institutions

Global and local technical changes: A new decomposition of the malmquist productivity index using virtual units

Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines: a finite-sample study

Algunos textos de apoyo para la Revista Logos Ciencia & Tecnología

Decomposing Economic Efficiency into Technical and Allocative Components: An Essential Property

Juan Aparicio Information

University

Position

Full Professor in Statistics & Operations Research

Citations(all)

3293

Citations(since 2020)

2250

Cited By

1776

hIndex(all)

32

hIndex(since 2020)

29

i10Index(all)

82

i10Index(since 2020)

66

Email

University Profile Page

Universidad Miguel Hernández de Elche

Google Scholar

View Google Scholar Profile

Juan Aparicio Skills & Research Interests

Efficiency

Productivity

Data Envelopment Analysis

Machine learning

Top articles of Juan Aparicio

Title

Journal

Author(s)

Publication Date

Estimating production functions through additive models based on regression splines

European Journal of Operational Research

Victor J España

Juan Aparicio

Xavier Barber

Miriam Esteve

2024/1/16

The defence economy: an assessment of productivity change in NATO countries

Applied Economics

Mónica Domínguez

Juan Aparicio

Antonio Fonfria

2024/4/14

Evaluating different methods for ranking inputs in the context of the performance assessment of decision making units: A machine learning approach

Computers & Operations Research

Daniel Valero-Carreras

Raul Moragues

Juan Aparicio

Nadia M Guerrero

2024/3/1

Benchmarking performance through efficiency analysis trees: Improvement strategies for colombian higher education institutions

Socio-Economic Planning Sciences

Jose Luis Zofio

Juan Aparicio

Javier Barbero

Jon Mikel Zabala-Iturriagagoitia

2024/2/22

Global and local technical changes: A new decomposition of the malmquist productivity index using virtual units

Economic Modelling

Juan Aparicio

Daniel Santín

2024/2/16

Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines: a finite-sample study

Operational Research

Raul Moragues

Juan Aparicio

Miriam Esteve

2023/9

Algunos textos de apoyo para la Revista Logos Ciencia & Tecnología

Revista Logos Ciencia & Tecnología

Juan Aparicio

2023

Decomposing Economic Efficiency into Technical and Allocative Components: An Essential Property

Journal of Optimization Theory and Applications

Juan Aparicio

José L Zofío

Jesús T Pastor

2023/4

A general direct approach for decomposing profit inefficiency

Omega

Jesus T Pastor

José Luis Zofío

Juan Aparicio

D Pastor

2023/9/1

How to peel a data envelopment analysis frontier: A cross-validation-based approach

Journal of the Operational Research Society

Juan Aparicio

Miriam Esteve

2023/12/2

The standard reverse approach for decomposing economic inefficiency

Journal of the Operational Research Society

Jesus T Pastor

José L Zofío

Juan Aparicio

Fernando Borrás

2023/3/31

The influence of bottlenecks on innovation systems performance: Put the slowest climber first

Technological Forecasting and Social Change

Jose Luis Zofio

Juan Aparicio

Javier Barbero

Jon Mikel Zabala-Iturriagagoitia

2023/8/1

boostingDEA: A boosting approach to Data Envelopment Analysis in R

SoftwareX

Maria D Guillen

Juan Aparicio

Victor J España

2023/12/1

Gradient tree boosting and the estimation of production frontiers

Expert Systems with Applications

Maria D Guillen

Juan Aparicio

Miriam Esteve

2023/3/15

Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques

Mathematics

Raul Moragues

Juan Aparicio

Miriam Esteve

2023/6/5

An unsupervised learning-based generalization of Data Envelopment Analysis

Operations Research Perspectives

Raul Moragues

Juan Aparicio

Miriam Esteve

2023/12/1

Performance Evaluation of Decision-Making Units Through Boosting Methods in the Context of Free Disposal Hull: Some Exact and Heuristic Algorithms

International Journal of Information Technology & Decision Making

Maria D Guillen

Juan Aparicio

Miriam Esteve

2023/1/31

Decomposing profit change: Konüs, Bennet and Luenberger indicators

Socio-Economic Planning Sciences

Juan Aparicio

José L Zofío

2023/6/1

Measuring dynamic inefficiency through machine learning techniques

Expert Systems with Applications

Juan Aparicio

Miriam Esteve

Magdalena Kapelko

2023/10/15

Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull

European Journal of Operational Research

Miriam Esteve

Juan Aparicio

Jesus J Rodriguez-Sala

Joe Zhu

2023/1/16

See List of Professors in Juan Aparicio University(Universidad Miguel Hernández de Elche)

Co-Authors

H-index: 44
Jesús T. Pastor Ciurana

Jesús T. Pastor Ciurana

Universidad Miguel Hernández de Elche

H-index: 31
Jose Manuel Cordero Ferrera

Jose Manuel Cordero Ferrera

Universidad de Extremadura

H-index: 30
Daniel Santín

Daniel Santín

Universidad Complutense de Madrid

H-index: 28
José L. Zofío

José L. Zofío

Universidad Autónoma de Madrid

H-index: 25
JAIME DE PABLO VALENCIANO

JAIME DE PABLO VALENCIANO

Universidad de Almería

H-index: 22
Jose Juan López Espín

Jose Juan López Espín

Universidad Miguel Hernández de Elche

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