Nonlinear Effects of Agricultural Technology on Sustainable Grain Production in China

Problemy Ekorozwoju, Journal Year: 2024, Volume and Issue: 19(2), P. 91 - 105

Published: July 1, 2024

Grain production is an important element of the United Nations Sustainable Development Goals, regarding livelihoods and social stability. This article uses data on agricultural technology, factor grain in China from 2011 to 2022, Generalized Additive Model (GAM) deeply explore nonlinear impact technology production. The results study show that (1) China’s output generally a growing trend, but growth rate declining fluctuating significantly. There significant difference before after COVID-19 epidemic. Moreover, northern region significantly higher than south. (2) Except for Consumption expenditure per capita, all other variables are positively correlated with out. (3) shows non-linear characteristics, its effect varies different intervals. Specifically, When value meteorological observation service station 20-25, effective irrigation area greater 1800, consumption capita 17000 total sowing crops 7500, it can increase yield. On contrary, if emission chemical oxygen demand exceeds 130, has inhibitory Furthermore, yield peaks when power machinery, GDP, number unemployed people cities approach 3000, 10000, 20, respectively. provide basis optimizing allocation resources enhancing efficiency Finally, some practical policy recommendations put forward.

Language: Английский

Application of GLM and GAMLSS Models in Predictive Analysis of Motor Bodily Injury Claims

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 365 - 375

Published: Jan. 1, 2024

Language: Английский

تقدير عدد مطالبات تأمين السيارات التكميلي باستخدام النماذج التجميعية المعممة Generalized Additive Models وأساليب التسوية LASSO Regularization بالتطبيق على إحدى شركات التأمين العاملة في سوق التأمين المصري

Deleted Journal, Journal Year: 2023, Volume and Issue: 47(4), P. 1064 - 1087

Published: Oct. 1, 2023

This research aims to estimate the frequency of claims in private Motor Insurance industry Egypt by using Generalized Additive Models (GAMs), which can capture common non-linear complex relationships combining multiple smooth functions predictor variables additive models (AMs).The researchers proposed a model for pricing insurance that incorporates LASSO regularization methods into GAM models, increase predictive performance model.Additionally, this Research introduced new risk factors had not been taken consideration number Egyptian Market, Distances and I-SCORES credit rating.The used Line Business data from one companies market.To achieve objective, The statistical-analytical Machine learning with "mgcv" R package are used.The findings reveal L1 Techniques (ML) helps improving its interpretability results increasing performance, selecting most on Pricing insurance.

Language: Английский