Constructing fuzzy-statistical prediction intervals from crisp linear regression models

2019 ◽  
Author(s):  
Kingsley Adjenughwure ◽  
Basil Papadopoulos
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Prediction Intervals ◽  
Statistical Prediction ◽  
Linear Regression Models

scholarly journals Statistical prediction of Sri Lankan rainfall during October to December

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 491-502
Author(s):  
KARUNAPALA PABODINI ◽  
YOO CHANGHYUN
Keyword(s):  
Sri Lanka ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Partial Least Square ◽  
Statistical Prediction ◽  
Lead Times ◽  
Sri Lankan ◽  
Linear Regression Models ◽  
Multiple Linear Regression Models

Sri Lanka receives most rainfall during October to December (OND). Here we construct multiple linear regression models to forecast the OND Sri Lankan rainfall during 1979-2012 for lead times of 1 and 2 months. Correlation analysis was used to examine the relationship between Sri Lankan OND rainfall and global sea surface temperature (SST) anomalies. Three independent predictors were identified through partial least square regression method which includes the southern Atlantic SST tendency, southern Pacific SST tendency and western Pacific and Maritime Continent SST tendency at two different lead times. Three-year-out cross validation concludes that the multiple linear regression models can produce forecast the OND rainfall forecast at correlation coefficient skill of 0.69 and 0.68 for the 1 and 2 month lead times respectively. The physical processes associated with these three predictors show that they contribute to increase in OND rainfall of Sri Lanka.

Simple linear regression with interval censored dependent and independent variables

2016 ◽  
Vol 27 (1) ◽  
pp. 198-207 ◽  
Author(s):  
Marcel F de Lima Taga ◽  
Julio M Singer
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Maximum Likelihood Estimators ◽  
Prediction Intervals ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Independent Variables ◽  
Interval Censored

We consider a simple linear regression model that accommodates situations where both the dependent and the independent variables are interval censored. We obtain maximum likelihood estimators of its parameters and compare their performance with that of estimators derived under ordinary linear regression models. We also develop prediction intervals for the response and illustrate the results with data from an audiometric study designed to evaluate the possibility of prediction of behavioural thresholds from physiological thresholds.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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