A modification of the CUSUM test in the linear regression model with lagged dependent variables

1989 ◽  
Vol 14 (2) ◽  
pp. 65-75 ◽  
Author(s):  
W. Ploberger ◽  
W. Krämer ◽  
R. Alt
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Cusum Test ◽  
Dependent Variables

A cusum test in the linear regression model with serially correlated disturbances

1995 ◽  
Vol 14 (3) ◽  
pp. 331-346 ◽  
Author(s):  
Kao Chihwa ◽  
Stephen. L. Ross
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Cusum Test

scholarly journals Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

2021 ◽  
Vol 12 ◽  
Author(s):  
Sung-Woo Kim ◽  
Hun-Young Park ◽  
Hoeryong Jung ◽  
Jinkue Lee ◽  
Kiwon Lim
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Cardiorespiratory Fitness ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Muscular Endurance ◽  
Dependent Variables ◽  
Health Related

Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19–64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO2max). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was ≥2. In the regression model, the coefficient of determination for HGS (adjusted R2: 0.870, P < 0.001), muscular endurance (adjusted R2: 0.751, P < 0.001), and cardiorespiratory fitness (adjusted R2: 0.885, P < 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R2: 0.298, P < 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

scholarly journals Enterprise Power Consumption Data and GDP Forecasting Based on Ensemble Algorithms

2021 ◽  
Vol 233 ◽  
pp. 01030
Author(s):  
Yibin Xu ◽  
Lu He ◽  
Ying Liang ◽  
Jianhong Si ◽  
Yonglong Bao
Keyword(s):  
Linear Regression ◽  
Power Consumption ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Consumption Data ◽  
Dependent Variables ◽  
Ensemble Algorithms

This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.

scholarly journals The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh

2008 ◽  
Vol 2 (1) ◽  
Author(s):  
Mahdi Abrar
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Newcastle Disease ◽  
Linear Regression Model ◽  
Minimum Temperature ◽  
Multiple Linear Regression Model ◽  
Maximum Temperature ◽  
Independent Variables ◽  
Dependent Variables

The objective of this research is to see the influence of weather on the prevalence of Newcastle Disease (ND) in chicken in Kabupaten Aceh Utara (North Aceh). Data used in this research were obtained from Dinas Peternakan North Aceh for the number of chicken suffered ND and from Badan Meteorologi dan Geofisika Lhokseumawe, North Aceh for the form of weather. Multiple Linear Regression Model with five independent variables (the average of rainfall per month, the average of maximum temperature, the average of minimum temperature, the velocity of the wind, and the average of humidity per month) was used to see the influence of wheather to the prevalence of Newcastle Disease. Proportion the number of chicken suffered from ND which is the ratio of the number of chicken suffered from ND to the total number of chicken was used as dependent variables. The result shows that the best model is Ŷ= 120.529278 – 1.33 x wind humidity + 1.907 x wind velocity.

The Local Power of the CUSUM and CUSUM of Squares Tests

1990 ◽  
Vol 6 (3) ◽  
pp. 335-347 ◽  
Author(s):  
Werner Ploberger ◽  
Walter Krämer;
Keyword(s):  
Structural Change ◽  
Linear Regression ◽  
Regression Model ◽  
Wide Class ◽  
Linear Regression Model ◽  
Structural Changes ◽  
Local Power ◽  
Cusum Test

We consider the local power of the cusum and cusum of squares tests for structural change in the linear regression model. We show that the local power of the cusum of squares test equals its size for a wide class of structural changes, as compared to a nontrivial local power for the cusum test. The conventional ranking of these procedures is thus reversed.

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