scholarly journals Testing conditional mean through regression model sequence using Yanai’s generalized coefficient of determination

2021 ◽  
Vol 158 ◽  
pp. 107168
Author(s):  
Masao Ueki
Keyword(s):  
Regression Model ◽  
Coefficient Of Determination ◽  
Conditional Mean ◽  
Model Sequence

Estimation of nonstationary nonparametric regression model with multiplicative structure

2021 ◽  
Author(s):  
Likai Chen ◽  
Ekaterina Smetanina ◽  
Wei Biao Wu
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Risk Premium ◽  
Convergence Rates ◽  
Broad Class ◽  
Estimation Procedure ◽  
Small Samples ◽  
Nonparametric Regression Model ◽  
Conditional Mean ◽  
Mean Function

Abstract This paper presents a multiplicative nonstationary nonparametric regression model which allows for a broad class of nonstationary processes. We propose a three-step estimation procedure to uncover the conditional mean function and establish uniform convergence rates and asymptotic normality of our estimators. The new model can also be seen as a dimension-reduction technique for a general two-dimensional time-varying nonparametric regression model, which is especially useful in small samples and for estimating explicitly multiplicative structural models. We consider two applications: estimating a pricing equation for the US aggregate economy to model consumption growth, and estimating the shape of the monthly risk premium for S&P 500 Index data.

scholarly journals An Illustrated Guide to Estimation of Water Velocity in Unregulated River for Hydrokinetic Performance Analysis Studies in East Malaysia

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1330
Author(s):  
Almalik Mohd Saupi ◽  
Nashiren Mailah ◽  
Mohd Mohd Radzi ◽  
Kamarul Mohamad ◽  
Saiful Ahmad ◽  
...  
Keyword(s):  
Regression Model ◽  
Water Level ◽  
Rural Areas ◽  
Electrical Energy ◽  
Peninsular Malaysia ◽  
Water Velocity ◽  
Velocity Estimation ◽  
Coefficient Of Determination ◽  
Velocity Data ◽  
Model Equations

Electrification coverage in Sarawak is the lowest at 78.74%, compared to Peninsular Malaysia at 99.62% and Sabah at 82.51%. Kapit, Sarawak, with 88.4% of its population located in rural areas and mostly situated along the main riverbanks, has great potential to generate electrical energy with a hydrokinetic system. Yearly water velocity data is the most significant parameter with which to perform a hydrokinetic analysis study. Nevertheless, the data retrieved from local river databases are inadequate for river energy analysis, thus hindering its progression. Instead, flow rates and rainfall data had been utilized to estimate the water velocity data. Till present, there is still no publication has been found on estimating of water velocity data in unregulated river using water level. Therefore, a novel technique of estimating the daily average water velocity data in unregulated rivers is proposed. The modelling of regression equation for water velocity estimation was performed and two regression model equations were generated to estimate both water level and water velocity on-site and proven to be valid as the coefficient of determination values had been R2 = 87.4% and R2 = 87.9%, respectively. The combination of both regression model equations can be used to estimate long-term time series water velocity data for type-C unregulated river in remote areas.

Effect of Strategic Intent on Performance of Small and Medium Scale Printing Press Firms in Abuja, Nigeria

2020 ◽  
Vol 1 (2) ◽  
pp. 26-37
Author(s):  
Felicia Eze ◽  
Murat Akyüz ◽  
Opusunju Michael Isaac
Keyword(s):  
Central Bank ◽  
Regression Model ◽  
Mission Statement ◽  
Control Variable ◽  
Multiple Regression Model ◽  
Printing Press ◽  
Coefficient Of Determination ◽  
Access To Finance ◽  
Medium Scale ◽  
Strategic Intent

Purpose: This study investigates the effect of strategic intent on the performance of small and medium scale printing firms in Federal Capital Territory (FCT), Abuja, Nigeria. Methods: The population of the study included all the small and medium scale printing press in Abuja which is 226 and the sample size of 68. A multiple regression model was formulated to estimate the effect of strategic intent (vision, mission, and objectives) on performance (growth) of small and medium scale printing press firms in Abuja. The study also adopted a control variable such as finance to have a better coefficient of determination. Findings: The study found that strategic intent had a positive and significant effect on the growth of small and medium scale printing press firms in Nigeria. The study also found that finance (collateral, access to finance, and insufficient finance) had a negative and insignificant effect on the growth of small and medium scale printing press firms in Nigeria.  Implication: Small and medium printing press firms in Abuja, FCT should communicate their vision, mission statement, and objectives to their employees. The microfinance banks in collaboration with the central bank of Nigeria should minimize collateral conditions in obtaining microcredit from microfinance banks.   

Whole Facility Measurement for Quantifying Energy Saving in an Office Building, Malaysia

2015 ◽  
Vol 785 ◽  
pp. 676-681 ◽  
Author(s):  
Nor Shahida Razali ◽  
Nofri Yenita Dahlan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Energy Saving ◽  
Linear Regression Model ◽  
Energy Use ◽  
Multiple Linear Regression Model ◽  
Office Building ◽  
Coefficient Of Determination ◽  
Independent Variable

This paper presents the concept of International Performance Measurement and Verification Protocol (IPMVP) for determining energy saving at whole facility level for an office building in Malaysia. Regression analysis is used to develop baseline model from a set of baseline data which correlates baseline energy with appropriate independents variables, i.e. Cooling Degree Days (CDD) and Number of Working Days (NWD) in this paper. In determining energy savings, the baseline energy is adjusted to the same set condition of reporting period using energy cost avoidance approach. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression for each independent variable. Results show that NWD has coefficient of determination, R2 higher than CDD which indicates that NWD has stronger correlation with the energy use than CDD in the building. Finding also shows that the R2 for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Maximum Absolute Error ◽  
Data Points

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

scholarly journals The Influence of Service Quality, Price, and Brand Loyalty on Motorcycle Purchase Decision at Dealer CV. Andalas Motor Bengkalis

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Malika Nefti Budi Asih ◽  
Hutomo Atman Maulana
Keyword(s):  
Linear Regression ◽  
Service Quality ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Sample Size ◽  
Brand Loyalty ◽  
Coefficient Of Determination ◽  
Purchase Decision ◽  
Purchase Decisions ◽  
Purchasing Decisions

This study aims to determine the effect of Service Quality, Price, and Brand Loyalty on Motorcycle Purchase Decisions at dealer CV.Andalas Motor Bengkalis. This research is associative research. The population used by consumers who buy motorbikes at dealer CV Andalas Motor Bengkalis with a sample size of 100 respondents. The technique used is multiple linear regression with the regression model Y=1.821 + 0.163X1 + 0.299X2 + 0.207X3 + e. The results of this study indicate that partially service quality has a positive and significant effect on purchasing decisions with tcount 2.466, price has a positive and significant effect on purchasing decisions with tcount 4.045. Brand loyalty has a positive and significant effect on purchasing decisions with tcount 6.158. Simultaneously, Service Quality, Price, and Brand Loyalty have a positive and significant effect on motorcycle purchasing decisions at dealer CV.Andalas Motor Bengkalis with Fcount 67.837 seen from the coefficient of determination 67.9%.

scholarly journals Optimalisasi Pemahaman Fisika Pada Hukum Newton Dengan Inquiry - Heuristik Vee Berbantuan Alat Peraga

2020 ◽  
Vol 8 (1) ◽  
pp. 73
Author(s):  
Nurin Fitriana
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Learning Outcomes ◽  
Knowledge Construction ◽  
Pearson Correlation ◽  
Coefficient Of Determination ◽  
Outcomes Analysis ◽  
A Value ◽  
Pearson Correlation Test ◽  
Basic Physics

This study aims to improve the optimization of students in the ability to construct their knowledge in understanding the concepts of Basic Physics in particular Newton's Law with Inquiry-Heuristic Assisted Vee Aids. The type of research used in this study is the type of explanatory research and analysis used in this study using SPSS, Descriptive Interpretation, Pearson Correlation Interpretation, linear regression, ANOVA. The results showed that the Pearson correlation test showed a value of 0.666 shows a positive correlation between the ability of knowledge construction with learning outcomes. Analysis of the coefficient of determination is 0.444, which means that the influence of the ability of knowledge construction to learning outcomes is 44.4% with F value = 18,339. The regression model can be used to predict the participation variable. Thus it is stated that the ability of knowledge construction has a strong degree of closeness to learning outcomes.

scholarly journals A multiple regression model for prediction of optimal dose of Moringa Oleifera in faecal sludge dewatering

2021 ◽  
Author(s):  
Benjamin Doglas ◽  
Richard Kimwaga ◽  
Aloyce Mayo
Keyword(s):  
Regression Model ◽  
Moringa Oleifera ◽  
Multiple Linear Regression Model ◽  
Optimal Dose ◽  
Multiple Regression Model ◽  
Coefficient Of Determination ◽  
Septic Tank ◽  
Contributing Factors ◽  
Jar Test ◽  
The University

Abstract Moringa Oleifera (MO) is a highly effective conditioner in the dewatering of Fecal sludge (FS). However, the model for the prediction of its optimal dose has not yet been documented. This article presents the results of the developed model for the prediction of MO optimal doses. The developed model was based on assessing the FS parameters and MO stock solution. The FS samples were obtained from a mixture of a pit latrine and septic tank and were analyzed at the water quality laboratory of the University of Dar es Salaam. The multiple linear regression model was used to establish a relationship between MO optimal dose as a function of FS characteristics (pH, Electrical Conductivity, Total Solids and Total Suspended Solids) and concentration of MO stock solution. The results indicated that the main contributing factors which determine the MO optimal dose were the concentration of MO stock solution, followed by pH of FS. The model results showed a good agreement between the predicted and observed MO optimal dose with a coefficient of determination of R2 = 0.72 and 0.9 for calibration and validation respectively. Therefore, the model can be adapted to determine the MO optimal dose without running the Jar-test experiment.

scholarly journals PERAMALAN PENERIMAAN BEA CUKAI INDONESIA

2018 ◽  
Vol 2 (1) ◽  
pp. 137
Author(s):  
Yolanda Sari ◽  
Nurlia Fusfita
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Rational Approach ◽  
Ordinary Least Square ◽  
Export Duty ◽  
Double Exponential ◽  
Double Exponential Smoothing

The revenue of customs and excise is very important in APBN. By making accurate estimation, target of revenue can be better determined. In addition, the revenue of customs and excise is also influenced by many external factors that are difficult to predict therefore a rational approach is needed to estimate revenue. This research uses Double Exponential Smoothing, Ordinary Least Square (OLS) model and Moving Average in predicting customs and excise revenue. Data used in this research is secondary data in time coherent pattern. The data includes import duty, export duty and excise obtained from the Directorate General of Customs and excise (DJBC) in the form of annual and quarterly data. This data starts from 2002 to 2016 with out of sample from 2017 to 2019. Some of these models are compared to each other to obtain the best model, and from the best model is also obtained estimating results in 3 years ahead. This study shows that the Double Exponential Smoothing model is better for predicting import duties compared to OLS and Moving Average models, which are models that have the smallest Sum Square Error (SSE) value. While the export and excise duty is best estimated by using OLS model which is shown with coefficient of determination value (R2)  regression model of export duty is 0.8, while the excise regression model has coefficient of determination of 0.9.Keywords:  Customs Estimation, Double Exponential Smoothing, Ordinary Least Square, Moving Average

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