Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile

Figueroa-Zúñiga, Jorge; Toledo, Juan; Lagos-Álvarez, Bernardo; Leiva, Víctor; Navarrete, Jean P.

doi:10.3390/math11132894

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…For future research, we suggest conducting simulations with more inputs and outputs, as well as fewer decision-making units. It could also be worth considering a quantile regression model based on the Kumaraswamy distribution [38] instead of the beta regression model. This model could be a compelling alternative, especially when outliers are present in the response variable under consideration.…”

Section: Discussionmentioning

confidence: 99%

Robust Semi-Parametric Inference for Two-Stage Production Models: A Beta Regression Approach

Ospina,

Baltazar,

Leiva

et al. 2023

Symmetry

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The data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal.

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Section: Discussionmentioning

confidence: 99%

Robust Semi-Parametric Inference for Two-Stage Production Models: A Beta Regression Approach

Ospina,

Baltazar,

Leiva

et al. 2023

Symmetry

Self Cite

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“…In recent years. other approaches have also been developed, including those using artificial intelligence methods such as cluster analysis methods, e.g., clustering with the EM algorithm or the k-means method, as well as neural networks [9][10][11][12][13][14][15].…”

Section: Methods Of Grouping Objects Into Sets Of Similar Objectsmentioning

confidence: 99%

A New Method Using Artificial Neural Networks to Group Mines into Similar Sets for Efficient Management and Transformation

Wyganowska,

Bańka

2024

Applied Sciences

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The market economy means that only those companies that are characterised by the generation of positive economic results and liquidity can function, survive and thrive. Due to the importance of the coal industry in economic and social terms—due to the number of people employed in the coal industry—it is necessary to constantly search for methods to improve management and business efficiency. This paper proposes the use of artificial neural networks to group mines into sets of similar mines. These sets can be used to make different business decisions for these companies. These sites can be easily compared with each other, in search of the areas that need to be restructured. In addition, developing pro-efficiency strategies for designated groups of similar mines is simpler than for each mine individually. This reduces the number of such studies in real terms and allows effective business measures to be applied more quickly.

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“…The fundamental concept underlying this algorithm is the assumption that the dataset originates from an unobservable discrete random variable U, which signifies the mixture component responsible for generating each observation yi. The algorithm iteratively fits these probabilities, updating them in each iteration until a convergence criterion is met [12].…”

Section: Em Algorithmmentioning

confidence: 99%

Evaluating Clustering Algorithms: An Analysis using the EDAS Method

Siva Shankar,

Maithili,

Madhavi

et al. 2023

E3S Web Conf.

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Data clustering is frequently utilized in the early stages of analyzing big data. It enables the examination of massive datasets encompassing diverse types of data, with the aim of revealing undiscovered correlations, concealed patterns, and other valuable information that can be leveraged. The assessment of algorithms designed for handling large-scale data poses a significant research challenge across various fields. Evaluating the performance of different algorithms in processing massive data can yield diverse or even contradictory results, a phenomenon that remains insufficiently explored. This paper seeks to address this issue by proposing a solution framework for evaluating clustering algorithms, with the objective of reconciling divergent or conflicting evaluation outcomes. “The multicriteria decision making (MCDM) method” is used to assess the clustering algorithms. Using the EDAS rating system, the report examines six alternative clustering algorithms “the KM algorithm, EM algorithm, filtered clustering (FC), farthest-first (FF) algorithm, make density-based clustering (MD), and hierarchical clustering (HC)”—against, six clustering external measures. The Expectation Maximization (EM) algorithm has an ASi value of 0.048021 and is ranked 5th among the clustering algorithms. The Farthest-First (FF) Algorithm has an ASi value of 0.753745 and is ranked 2nd. The Filtered Clustering (FC) algorithm has an ASi value of 0.055173 and is ranked 4th. The Hierarchical Clustering (HC) algorithm has the highest ASi value of 0.929506 and is ranked 1st. The Make Density-Based Clustering (MD) algorithm has an ASi value of 0.011219 and is ranked 6th. Lastly, the K-Means Algorithm has an ASi value of 0.055376 and is ranked 3rd. These ASi values provide an assessment of each algorithm’s overall performance, and the rankings offer a comparative analysis of their performance. Based on the result, we observe that the Hierarchical Clustering algorithm achieves the highest ASi value and is ranked first, indicating its superior performance compared to the other algorithms.

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Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile

Cited by 3 publications

References 57 publications

Robust Semi-Parametric Inference for Two-Stage Production Models: A Beta Regression Approach

Robust Semi-Parametric Inference for Two-Stage Production Models: A Beta Regression Approach

A New Method Using Artificial Neural Networks to Group Mines into Similar Sets for Efficient Management and Transformation

Evaluating Clustering Algorithms: An Analysis using the EDAS Method

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