Parametric quantile regression based on the inverse Gaussian distribution

Journal of Statistical Computation and Simulation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: March 10, 2025

Language: Английский

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Fuzzy Non-Payment Risk Management Rooted in Optimized Household Consumption Units

Risks, Journal Year: 2025, Volume and Issue: 13(4), P. 74 - 74

Published: April 11, 2025

Traditionally, business risk management models have not taken into consideration household composition for the purposes of credit granting or project financing in order to manage default. In this research, an improvement model was obtained by introducing as a new exogenous variable. With application generalized reduced gradient nonlinear optimization modeling, improved consumption units are determined according different types size and age their members. Estimated economies scale show consistent pattern even year 2020, corresponding with COVID-19 outbreak. Thus, adjusted estimation equivalized disposable income is obtained. Based on more accurate estimation, acceptable debt levels can be determined. The probabilities default allows managed. way, novel proposed incorporating evaluation using fuzzy clustering techniques. Companies assess expected loss exposure through that help them process making evidence-informed decisions.

Language: Английский

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A Novel Regression Model for Fractiles: Formulation, Computational Aspects, and Applications to Medical Data

Fractal and Fractional, Journal Year: 2023, Volume and Issue: 7(2), P. 169 - 169

Published: Feb. 7, 2023

Covariate-related response variables that are measured on the unit interval frequently arise in diverse studies when index and proportion data of interest. A regression mean is commonly used to model this relationship. Instead relying mean, which sensitive atypical less general, we can estimate such a relation using fractile regression. point probability density curve area under between origin equal specified fraction. Fractile or quantile modeling has been considered for some statistical distributions. Our objective present article formulate novel based parametric distribution. developed reparameterizing initial Then, introduce functional form through link function. The main features new distribution, as well density, functions, obtained. We consider brand-new distribution fractiles continuous dependent variable (response) bounded (0, 1). discuss an R package with random number generators functions cumulative quantile, addition estimation checking. original distribution-free regression, lately employed several investigations. use fit apply it two case COVID-19 medical from Brazil United States illustration.

Language: Английский

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On Huber's Robust Technique and Quantile Regression Models for the Total Production of Field Crops in Oman

Universal Journal of Agricultural Research, Journal Year: 2024, Volume and Issue: 12(2), P. 429 - 444

Published: April 1, 2024

In this paper, the problem of modelling data agriculture total production Field Crops, agricultural land area, temperature and humidity is studied, collected (for period 1999-2020) two different models are developed, i.e. univariate multivariate based on Huber loss robust technique quantile regression.A transformed set used to mitigate impact skewness stabilize variance.This study evaluates significance goodness fit above random variables.Moreover, several assumptions testing hypotheses conducted identify behaviour data.Due size data, bootstrap approach utilized verify predictability, fit, uniqueness estimations.Furthermore, prediction accuracy univariate/multivariate was resilient when compared each model, it discovered that both well.A 95% interval generated shown be valid for models.Based real example, turned out predictive regression representative not significant models.When comparing indices in models, RR sixth QR were found best fits data.The recommendation, limitations future research discussed.

Language: Английский

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Quasi-Cauchy Regression Modeling for Fractiles Based on Data Supported in the Unit Interval

Fractal and Fractional, Journal Year: 2023, Volume and Issue: 7(9), P. 667 - 667

Published: Sept. 4, 2023

A fractile is a location on probability density function with the associated surface being proportion of such function. The present study introduces novel methodological approach to modeling data within continuous unit interval using or quantile regression. This has unique advantage as it allows for direct interpretation response variable in relation explanatory variables. new provides robustness against outliers and permits heteroscedasticity be modeled, making tool analyzing datasets diverse characteristics. Importantly, our does not require assumptions about distribution variable, offering increased flexibility applicability across variety scenarios. Furthermore, addresses mitigates criticisms limitations inherent existing methodologies, thereby giving an improved framework interval. We validate effectiveness introduced two empirical applications, which highlight its practical utility superior performance real-world settings.

Language: Английский

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Quantile Regression with a New Exponentiated Odd Log-Logistic Weibull Distribution

Mathematics, Journal Year: 2023, Volume and Issue: 11(6), P. 1518 - 1518

Published: March 21, 2023

We define a new quantile regression model based on reparameterized exponentiated odd log-logistic Weibull distribution, and obtain some of its structural properties. It includes as sub-models known models that can be utilized in many areas. The maximum likelihood method is adopted to estimate the parameters, several simulations are performed study finite sample properties estimators. applicability proposed well justified by means gastric carcinoma dataset.

Language: Английский

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Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach

International Journal of Energy Economics and Policy, Journal Year: 2024, Volume and Issue: 14(4), P. 186 - 194

Published: July 5, 2024

This research develops a new electric charge prediction method by using Convolutional Neural Networks with Quantile Regression (CNN-QR) combined the Rainbow Technique for Categorical Features (RTCF) and Deep Learning to create layers architecture of neural network. combination captures both local global interdependencies within load data. In particular, RTCF employs advanced natural language processing (NLP) techniques convert several important categorical features into single feature called “category,” which is tailored various attributes Baja California Sur system, in Mexico, taking consideration climatic conditions, circumstances significant increase energy consumption. Furthermore, this compares CNN-QR classical quantile regression shows that works better at capturing sophisticated patterns producing probabilistic estimates. The above methodology uses hourly data from January 2019 October 2020. results obtained provide valuable information policy formulation sector, specifically areas forecasting expansion renewable electricity Finally, it worth mentioning utilization not only improves accuracy forecasting, but also provides strategic framework management resource planning dynamic systems, demonstrates its substantial importance market participants authorities, as well regulators.

Language: Английский

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Novos modelos de regressão e algoritmos de aprendizado de máquina: teoria e aplicações

Published: Jan. 30, 2024

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