A new approach using the genetic algorithm for parameter estimation in multiple linear regression with long-tailed symmetric distributed error terms: An application to the Covid-19 data

Yalçınkaya, Abdullah; Balay, İklim Gedik; Şenoğlu, Birdal

doi:10.1016/j.chemolab.2021.104372

Cited by 12 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(2) Establishing an expression for the fitness function that is feasible and accurately describes the relationship between the individuals of the population and the problem constraints. (3) Determining the genetic mechanisms such as replication, crossover, and mutation in the genetic algorithm process and the stopping conditions of the algorithm [27][28][29][30].…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Study on the Optimization of Agricultural Production Waste Recycling Network under the Concept of Green Cycle Development

et al. 2022

View full text Add to dashboard Cite

This study is based on the concept of converting agricultural waste into green new energy, we combine the concept of green cycle development and the relevant theories in modern system engineering to optimize the study of agricultural production waste recycling network. In this paper, the optimization of the agricultural production waste recycling network is divided into two aspects—facility site selection and vehicle path planning—with the objectives of agricultural production waste green recycling and the minimization of system construction and operational costs. In this study, the site selection and path planning problems were unified and an optimization model for the agricultural production waste recycling network site-path (LRP) problem was constructed. The optimization results of agricultural production waste recycling network facility location and recycling vehicle path planning were obtained by using the simulation data in the optimization model and designing the genetic algorithm design with the relevant characteristics of agricultural production waste recycling. The feasibility and operability of the model were verified through experiments. The research related to the optimization of agricultural production waste recycling networks can be used to both reduce production costs in agricultural areas and progress the practical theory of reverse logistics in agricultural areas. Agricultural waste resource utilization provides important support for the development of an ecological agriculture cycle and helps protect the environment.

show abstract

Section: Algorithm Descriptionmentioning

confidence: 99%

Study on the Optimization of Agricultural Production Waste Recycling Network under the Concept of Green Cycle Development

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Pandemi covid-19 tersebar ke belahan dunia selama dua tahun terakhir, termasuk di Indonesia (1) . Penyebaran virus covid-19 di beberapa negara telah diprediksi melalui beberapa metode statistik (2) .…”

Section: Pendahuluanunclassified

Pengembangan Model Perilaku Mencuci Tangan Pada Mahasiswa Untuk Pemulihan Kesehatan Lingkungan Kampus Pasca Pandemi Covid-19

Kusdarini

Budianto

2022

j. kesehat. lingkung.

View full text Add to dashboard Cite

Pembelajaran tatap muka memerlukan beberapa persiapan untuk mencegah berkembangnya kembali Covid-19. Mahasiswa adalah mayoritas dari penghuni kampus yang terlibat dalam proses pembelajaran sehingga kepatuhan mahasiswa dalam melaksanakan protokol kesehatan sangatlah penting. Protokol kesehatan mengharuskan cuci tangan sebagai bagian aktifitas penting. Penelitian ini bertujuan untuk mengembangkan model perilaku mencuci tangan dan menguji pengaruh variabel pengetahuan (Z1), sikap (Z2), dan sarana sabun, air, hand sanitizer (Z3) terhadap perilaku mencuci tangan (Y). Pengujian model menggunakan satu set data empiris yang dikumpulkan dari 101 mahasiswa jurusan teknik kimia secara acak. Selanjutnya data dikumpulkan, diurutkan, dan dianalisa secara statisik menggunakan statistik deskriptif, uji regresi linier berganda, uji T, dan uji F. Representasi data, validasi dan korelasi data dilakukan dalam MS Excel dan SPSS 20. Uji regresi linier berganda menghasilkan persamaan Y = 3,722 + 0,035 Z1 + 0,142 Z2 + 0,269 Z3 + e. Uji T menunjukkan bahwa pengetahuan tidak mempengaruhi secara parsial perilaku mencuci tangan; baik sikap maupun sarana, masing-masing mempengaruhi secara parsial perilaku mencuci tangan. Uji F menunjukkan bahwa pengetahuan, sikap, dan sarana mempengaruhi secara serentak perilaku mencuci tangan. Pengujian koefisien determinasi dari model menunjukkan bahwa pengetahuan, sikap, dan sarana sabun, air, hand sanitizer memberikan kontribusi 48,3% dari seluruh variabel yang mempengaruhi perilaku mencuci tangan. Pengembangan perilaku mencuci tangan dapat dilakukan dengan pemberian stimulus, antara lain sosialisasi pentingnya mencuci tangan (melalui poster, banner, penyuluhan di sela perkuliahan), penyediaan sarana sabun, air, dan hand sanitizer di sudut-sudut kampus, dan pelibatan mahasiswa dalam kegiatan sosialisasi maupun penyediaan sarana cuci tangan.

show abstract

“…For instance, the GA algorithm was applied to find the ML estimators of the skew normal distribution parameters, then compared with other numerical algorithms and showed the best performance [18]. Also, GA was used to find the ML estimators of the Weibull distribution and various regression model parameters [19,20] and GA was used in the first stage as a starting point to obtain the final posterior ML estimates for logit models [21]. It is further employed to estimate the ML values for the parameters of a cosmological model by maximizing its likelihood function [22].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Meta-Heuristic Algorithms for Maximum Likelihood Estimation of the Exponentially Modified Logistic Distribution

Kasap,

Faouri

2024

Symmetry

View full text Add to dashboard Cite

Generalized distributions have been studied a lot recently because of their flexibility and reliability in modeling lifetime data. The two-parameter Exponentially-Modified Logistic distribution is a flexible modified distribution that was introduced in 2018. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, log-logistic, and others. In this study, the unknown parameters of the Exponentially-Modified Logistic distribution are estimated using the maximum likelihood method. Five meta-heuristic algorithms, including the genetic algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and sine cosine algorithm, are applied in order to solve the nonlinear likelihood equations of the study model. The efficiencies of all maximum likelihood estimates for these algorithms are compared via an extensive Monte Carlo simulation study. The performance of the maximum likelihood estimates for the location and scale parameters of the Exponentially-Modified Logistic distribution developed with the genetic algorithm and grey wolf optimization algorithms is the most efficient among others, according to simulation findings. However, the genetic algorithm is two times faster than grey wolf optimization and can be considered better than grey wolf optimization considering the computation time criterion. Six real datasets are analyzed to show the flexibility of this distribution.

show abstract

A new approach using the genetic algorithm for parameter estimation in multiple linear regression with long-tailed symmetric distributed error terms: An application to the Covid-19 data

Cited by 12 publications

References 33 publications

Study on the Optimization of Agricultural Production Waste Recycling Network under the Concept of Green Cycle Development

Study on the Optimization of Agricultural Production Waste Recycling Network under the Concept of Green Cycle Development

Pengembangan Model Perilaku Mencuci Tangan Pada Mahasiswa Untuk Pemulihan Kesehatan Lingkungan Kampus Pasca Pandemi Covid-19

Comparison of the Meta-Heuristic Algorithms for Maximum Likelihood Estimation of the Exponentially Modified Logistic Distribution

Contact Info

Product

Resources

About