Distribution-based Regression Models for Semi-Bounded Data Analysis

2020 ◽  
Author(s):  
Pantea Koochemeshkian ◽  
Nuha Zamzami ◽  
Nizar Bouguila
Keyword(s):  
Data Analysis ◽  
Regression Models ◽  
Bounded Data

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

Flexible quasi-beta regression models for continuous bounded data

2018 ◽  
Vol 19 (6) ◽  
pp. 617-633 ◽  
Author(s):  
Wagner H Bonat ◽  
Ricardo R Petterle ◽  
John Hinde ◽  
Clarice GB Demétrio
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Beta Regression ◽  
Estimating Function ◽  
Dispersion Parameters ◽  
Linear Predictor ◽  
Mean And Variance ◽  
The Mean ◽  
Bounded Data ◽  
Beta Regression Model

We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters respectively. The flexible quasi-beta regression model can automatically adapt to the underlying bounded data distribution by the estimation of the power parameter. Furthermore, the model can easily handle data with exact zeroes and ones in a unified way and has the Bernoulli mean and variance relationship as a limiting case. The computational implementation of the proposed model is fast, relying on a simple Newton scoring algorithm. Simulation studies, using datasets generated from simplex and beta regression models show that the estimating function estimators are unbiased and consistent for the regression coefficients. We illustrate the flexibility of the quasi-beta regression model to deal with bounded data with two examples. We provide an R implementation and the datasets as supplementary materials.

Data Analysis Using Regression Models: The Business Perspective

1997 ◽  
Vol 51 (2) ◽  
pp. 209
Author(s):  
David E. Booth ◽  
Edward W. Frees
Keyword(s):  
Data Analysis ◽  
Regression Models ◽  
Business Perspective

Haar Wavelets in Data Analysis

2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Approximation Theory ◽  
Regression Models ◽  
Haar Wavelets ◽  
General Regression

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.

scholarly journals PENGARUH TENOR, UKURAN, DAN SPESIALISASI KANTOR AKUNTAN PUBLIK TERHADAP KUALITAS AUDIT

2019 ◽  
Vol 2 (2) ◽  
pp. 463
Author(s):  
Tjun Tjun
Keyword(s):  
Data Analysis ◽  
Multiple Regression ◽  
Firm Size ◽  
Regression Models ◽  
Audit Quality ◽  
Manufacturing Companies ◽  
Audit Firm ◽  
Multiple Regression Models

This study is to examine how far audit firm tenure, audit firm size and audit firm specialist influenced audit quality partially and simultaneously. This study used manufacturing companies in Indonesian Stock of Exhanges during period 2012-2016 as samples. Data analysis conducted with multiple regression models. The result proved that audit firm tenure, audit firm size and audit firm specialist influenced audit quality simultaneously. This study proved that audit firm tenure and audit firm specialist influenced audit quality partially, meanwhile audit firm size did not influenced audit quality.

scholarly journals PENGARUH EKSPOR IMPOR KAKAO DAN KARET TERHADAP CADANGAN DEVISA DI INDONESIA

2020 ◽  
Vol 3 (2) ◽  
pp. 34
Author(s):  
Mustafa Mustafa ◽  
Devi Andriyani
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Foreign Exchange ◽  
Regression Models ◽  
Secondary Data ◽  
Analysis Method ◽  
Linear Regression Models ◽  
Foreign Exchange Reserves ◽  
Multiple Linear Regression Models

This study aims to analyze the effect of cocoa and rubber export imports on foreign exchange reserves in Indonesia. This study uses secondary data from 2005-2017 obtained from the Central Bureau of Statistics of Indonesia. The data analysis method used multiple linear regression models. The results partially show that cocoa and rubber export impors do not significantly influence the foreign exchange reserves in Indonesia. Simultaneous cocoa and rubber export imports have a positive and significant effect on foreign exchange reserves in Indonesia. The amount of influence is 0,9059 or 90,59% while the rest is influenced by other variables outside the model by 09,41%.

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