Power of Tests for Nonlinear Transformation in Regression Analysis

1994 ◽  
Vol 10 (2) ◽  
pp. 357-371 ◽  
Author(s):  
Masahito Kobayashi
Keyword(s):  
Monte Carlo ◽  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Alternative Hypothesis ◽  
Nonlinear Transformation ◽  
Monte Carlo Experiment ◽  
Local Power ◽  
Power Of Tests ◽  
Extended Projection

This paper compares the local power of tests for a nonlinear transformation of the dependent variable in a regression model against the alternative hypothesis of a linear transformation. It is shown that the local power of the Cox test is higher than those of the extended projection test of MacKinnon, White, and Davidson, and Bera and McAleer's test. The theoretical result is supported by a Monte-Carlo experiment in testing for a regression model with a logarithmically transformed dependent variable against a linear regression model.

A Nonparametric Conditional Moment Test for Structural Stability

1995 ◽  
Vol 11 (4) ◽  
pp. 671-698 ◽  
Author(s):  
Javier Hidalgo
Keyword(s):  
Monte Carlo ◽  
Structural Change ◽  
Null Hypothesis ◽  
Asymptotic Efficiency ◽  
Econometric Model ◽  
Alternative Hypothesis ◽  
Structural Instability ◽  
Monte Carlo Experiment ◽  
Local Power ◽  
Conditional Moment

This paper considers a nonparametric conditional moment test of stability of an econometric model against the alternative of instability. The alternative hypothesis allows for more than one structural change, although in this case it has to be fairly smooth. This complements existing results for stability in a parametric setting. Also, it is shown that the test is always consistent, unlike the available “parametric” tests, which normally rely on the assumption of a correct specification of the model, at least under the null hypothesis of no structural instability. Moreover, we show that the test has local power comparable to the parametric ones; that is, its asymptotic efficiency is greater than zero. A Monte Carlo experiment about the performance of our test is described.

scholarly journals Cluster and regression analysis for predicting salinity in groundwater

2018 ◽  
Vol 192 ◽  
pp. 02007
Author(s):  
Phiraphat Aphiphan ◽  
Uma Seeboonruang ◽  
Somyot Kaitwanidvilai
Keyword(s):  
Electrical Conductivity ◽  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Water Levels ◽  
Groundwater Salinity ◽  
Groundwater Levels

Groundwater salinity is a major problem particularly in the northeastern region of Thailand. Saline groundwater can cause widespread saline soil problem resulting in reducing agricultural productivity as in the Lower Nam Kam River Basin. In order to better manage the salinity problem, it is important to be able to predict the groundwater salinity. The objective of this research was to create a cluster-regression model for predicting the groundwater salinity. The indicator of groundwater salinity in this study was electrical conductivity because it was simple to measure in field. Ninety-eight parameters were measured including precipitation, surface water levels, groundwater levels and electrical conductivity. In this study, the highest groundwater salinity at 3 wells was predicted using the combined cluster and multiple linear regression analysis. Cross correlation and cluster analysis were applied in order to reduce the number of parameters to effectively predict the quality. After the parameter selection, multiple linear regression was applied and the modeling results obtained were R2 of 0.888, 0.918, and 0.692, respectively. This linear regression model technique can be applied elsewhere in the similar situation.

scholarly journals A note on the Gao et al. (2019) uniform mixture model in the case of regression

2019 ◽  
Vol 289 (2) ◽  
pp. 495-501
Author(s):  
Mike G. Tsionas ◽  
Athanasios Andrikopoulos
Keyword(s):  
Monte Carlo ◽  
Linear Regression ◽  
Mixture Model ◽  
Bayesian Methods ◽  
Error Term ◽  
Probability Distributions ◽  
Monte Carlo Experiment ◽  
Link Type ◽  
Irregular Data ◽  
Weighted Combination

AbstractWe extend the uniform mixture model of Gao et al. (Ann Oper Res, 2019. 10.1007/s10479-019-03236-9) to the case of linear regression. Gao et al. (Ann Oper Res, 2019. 10.1007/s10479-019-03236-9) proposed that to characterize the probability distributions of multimodal and irregular data observed in engineering, a uniform mixture model can be used. This model is a weighted combination of multiple uniform distribution components. This case is of empirical interest since, in many instances, the distribution of the error term in a linear regression model cannot be assumed unimodal. Bayesian methods of inference organized around Markov chain Monte Carlo are proposed. In a Monte Carlo experiment, significant efficiency gains are found in comparison to least squares justifying the use of the uniform mixture model.

scholarly journals The Effect of Phase on Paper Thickness Optimization in the Paper Recycling Process Using Recycling Machine

2020 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fauzhia Rahmasari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Thickness Optimization ◽  
Recycling Process ◽  
Paper Recycling ◽  
Independent Variables ◽  
The Relationship

AbstractEfforts to manage the recycling of paper waste into new paper have been carried out in recent times. It takes a tool or machine that is able to effectively and efficiently recycle used paper into new paper. There are several factors that affect the effectiveness of paper recycling machines, one of which is the paper thickness. One method that can be used to analyze the factors that influence paper thickness in the paper production process using a paper recycling machine is regression analysis. Regression analysis is data analysis techniques in statistics that is used to examine the relationship between several independent variables and dependent variable. However, if we want to examine the relationship or effect of two or more independent variables on a dependent variable, the regression model used is a multiple linear regression model. This study purposes are to analyze the factors that influence paper thickness using a paper recycling machine using multiple linear regression and to inform the modeling about that. The results showed that the factors that affect the paper thickness optimization are destruction and press phase. AbstractUpaya pengelolaan daur ulang sampah kertas menjadi kertas baru telah banyak dilakukan pada jaman sekarang. Dibutuhkan suatu alat atau mesin yang mampu secara efektif dan efisien dalam mendaur ulang kertas bekas menjadi kertas baru. Terdapat beberapa faktor yang mempengaruhi tingkat efektifitas mesin daur ulang kertas diantaranya adalah ketebalan kertas. Salah satu metode yang dapat digunakan untuk menganalisis faktor-faktor yang mempengaruhi ketebalan kertas pada proses produksi kertas menggunakan mesin daur ulang kertas adalah analisis regresi. Analisis regresi merupakan teknik analisis data dalam statistika yang digunakan untuk mengkaji hubungan antara beberapa variabel bebas dengan variabel tidak bebas. Namun, jika ingin mengkaji hubungan atau pengaruh dua atau lebih variabel bebas terhadap satu variabel tidak bebas, maka model regresi yang digunakan adalah model regresi linier berganda. Tujuan dalam penelitian ini yaitu menganalisis faktor-faktor yang mempengaruhi ketebalan kertas menggunakan mesin daur ulang kertas menggunakan regresi linier berganda serta memberikan informasi pemodelan mengenai hal tersebut. Hasil penelitian menunjukkan bahwa faktor yang mempengaruhi keoptimalan ketebalan kertas adalah fase penghancuran dan pemadatan kertas

scholarly journals ACADEMIC ATMOSPHERE, PROVISION OF FACILITIES AND ITS EFFECT ON THE SPIRIT OF PERMANENT LECTURERS AT STIE TRISNA NEGARA OKU TIMUR

2020 ◽  
Vol 4 (03) ◽  
Author(s):  
Winda Feriyana
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Correlation Coefficient ◽  
Confidence Level ◽  
Analytical Method ◽  
Linear Regression Equation ◽  
Spss Software ◽  
Dominant Influence

This research aims to partially and collectively analyze the influence of the academic atmosphere, the provision of facilities on the work spirit of permanent lecturers and to analyze the most dominant variable in influencing the work spirit of permanent lecturers at STIE Trisna Negara OKU Timur. The regression analysis results and the correlation between academic atmosphere and employee work spirit show the regression model Ŷ = 21,908 + 0.551X1 + e with a correlation coefficient of 0.589. The results of the regression analysis and the correlation between the provision of facilities on employee work spirit show the regression model Ŷ = 46.446 + 0.027X2 + e with a correlation coefficient of 0.033. The results of multiple regression analysis and the correlation between the academic atmosphere and the provision of facilities together on work spirit show the regression model Ŷ = 19.792 + 0.553X1 + 0.043X2 + e with a correlation coefficient of 0.592 at the 95% confidence level, it is found that the academic atmosphere and provision facilities can simultaneously predict employee work spirit. From the multiple linear regression equation above, it shows that the academic atmosphere variable (X1) has a more dominant influence on employee work spirit than the provision of facilities. This research was conducted on 45 respondents with the analytical method used is path analysis using SPSS software. Keywords : Academic Atmosphere, Facility Provision, Work spirit

scholarly journals PENGARUH TRUSTWORTHINESS, EXPERTISE DAN ATTRACTIVENESS CELEBRITY ENDORSER DI INSTAGRAM TERHADAP PURCHASE INTENTION PRODUK HIJAB (Studi Kasus pada Akun Instagram @gitasav)

JEMBATAN ◽  
2019 ◽  
Vol 16 (1) ◽  
pp. 13-30
Author(s):  
Syarifah Fatimah Dina Najib H.A ◽  
Islahuddin Daud ◽  
Aslamia Rosa
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Purchase Intention ◽  
Sampling Method ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Multiple Linear Regression Analysis ◽  
Analysis Technique ◽  
Celebrity Endorser

This study was aimed to examine the effects of trustworthiness, expertise, and attractiveness of celebrity endorser on Instagram to purchase intention of hijab products. The sampling technique was done by purposive sampling method. The population in this study was followers of the @gitasav Instagram account. Data was collected through distributing questionnaires to 100 respondents. The analysis technique used in this study is multiple linear regression analysis. The results showed that simultaneously the variables of trustworthiness, expertise, and attractiveness had a significant effect on purchase intention. However, partially the purchase intention variable is only influenced by the trustworthiness variable which is equal to 3,878. In this study a regression model was obtained, which is Y = 10,021 + 0,655X1 + 0,038X2 - 0,122X3. 

scholarly journals COVID 19: THE IMPACT OF DEMAND, SUPPLY, AND LABOR

2020 ◽  
Vol 4 (5) ◽  
pp. 60-73
Author(s):  
Hantono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Data Collection ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Independent Variables ◽  
The Impact

This study aims to determine the effect of 1) demand, 2) supply, 3) labor, 4) covid 19. The sampling in this research was conducted by using a incidental sampling method. Methods of data collection through questionnaires that have been distributed to 100 respondents who have met criteria. With multiple linear regression analysis, it shows that the demand, supply, labor both partial and simultaneous have significant effect on covid 19. It can be concluded that mitigation of demand, supply, labor towards covid 19. The results of t test showed that demand is approved and indicates demand has great impact on affecting the covid 19, supply is not approved and indicates supply has less impact on affecting the covid 19, labor is approved and indicates labor has great impact on affecting the covid 19. The results of f test showed that both of the independent variables are simultaneously affecting the covid 19. The result of R Square of the regression model is 0.216 which shows that 21,6 % of mitigation of covid 19 can be explained by demand, supply, labor. Whereas, the 78,4% of covid 19 variable can be explained by other factors or variables which are not examined in this research.

An Introduction to the Augmented Inverse Propensity Weighted Estimator

2010 ◽  
Vol 18 (1) ◽  
pp. 36-56 ◽  
Author(s):  
Adam N. Glynn ◽  
Kevin M. Quinn
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Regression Model ◽  
Interesting Property ◽  
Outcome Variable ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Propensity Score Model ◽  
Matching Estimator ◽  
Score Model

In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called “double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.

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