scholarly journals Modeling maximum daily temperature using a varying coefficient regression model

2014 ◽  
Vol 50 (4) ◽  
pp. 3073-3087 ◽  
Author(s):  
Han Li ◽  
Xinwei Deng ◽  
Dong-Yun Kim ◽  
Eric P. Smith
Keyword(s):  
Regression Model ◽  
Daily Temperature ◽  
Maximum Daily Temperature ◽  
Varying Coefficient ◽  
Varying Coefficient Regression

scholarly journals Clustered spatio‐temporal varying coefficient regression model

2020 ◽  
Vol 40 (2) ◽  
pp. 465-480
Author(s):  
Junho Lee ◽  
Maria E. Kamenetsky ◽  
Ronald E. Gangnon ◽  
Jun Zhu
Keyword(s):  
Regression Model ◽  
Varying Coefficient ◽  
Varying Coefficient Regression ◽  
Spatio Temporal

Study on Forecasting of Gold Price Based on Varying-Coefficient Regression Model

2011 ◽  
Vol 467-469 ◽  
pp. 1398-1403
Author(s):  
Qi Zhang ◽  
Jun Hai Ma ◽  
Yan Wang
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Cross Validation ◽  
Least Squares Method ◽  
Weighting Function ◽  
Weighted Least Squares ◽  
Gold Price ◽  
Varying Coefficient ◽  
Sample Data ◽  
Varying Coefficient Regression

U.S. dollar index, oil prices, silver prices, DOW index, OECD leading index and the CRB index are selected and varying-coefficient regression model which has dynamic response to the various variables influence is applied to predict the gold price and improve the prediction accuracy in this paper. In addition, the weighted least squares is adopted as an estimation of the parameters, corrects the traditional least squares method defect which assumes the sample data weights equal points to the prediction, making sample weights larger closer with prediction points. In the choice of weighting function, the paper uses cross validation to gain smoothing parameter. In the last, we predicted the 12 months gold prices from January 2010 December 2010 applies varying-coefficient regression model.

Estimation of the error distribution in a varying coefficient regression model

2018 ◽  
Vol 30 (2) ◽  
pp. 392-429 ◽  
Author(s):  
Anton Schick ◽  
Yilin Zhu ◽  
Xiaojie Du
Keyword(s):  
Regression Model ◽  
Error Distribution ◽  
Varying Coefficient ◽  
Varying Coefficient Regression

Quantile Fuzzy Varying Coefficient Regression based on kernel function

2021 ◽  
Vol 107 ◽  
pp. 107313
Author(s):  
Amir Hamzeh Khammar ◽  
Mohsen Arefi ◽  
Mohammad Ghasem Akbari
Keyword(s):  
Kernel Function ◽  
Varying Coefficient ◽  
Varying Coefficient Regression

The application of Dynamic Linear Bayesian Models in hydrological forecasting: Varying Coefficient Regression and Discount Weighted Regression

2015 ◽  
Vol 530 ◽  
pp. 762-784 ◽  
Author(s):  
Maurycy Ciupak ◽  
Bogdan Ozga-Zielinski ◽  
Jan Adamowski ◽  
John Quilty ◽  
Bahaa Khalil
Keyword(s):  
Bayesian Models ◽  
Weighted Regression ◽  
Varying Coefficient ◽  
Hydrological Forecasting ◽  
Varying Coefficient Regression

Estimating water consumption and factors affecting intake in grazing cattle

1994 ◽  
Vol 74 (3) ◽  
pp. 551-554 ◽  
Author(s):  
Shaukat Ali ◽  
L. A. Goonewardene ◽  
J. A. Basarab
Keyword(s):  
Relative Humidity ◽  
Key Words ◽  
Water Consumption ◽  
Important Determinant ◽  
Daily Temperature ◽  
Maximum Daily Temperature ◽  
Factors Affecting ◽  
Percentage Decrease ◽  
Grazing Cattle

Water consumption (WC) by 39.5 animal units (AU) of grazing cattle was studied at a central Alberta site in summer. Average WC AU−1 was estimated at 48.9 L d−1. WC increased by 0.68 L AU−1 (P < 0.01) for each percentage decrease in relative humidity, increased by 0.81 L AU−1 (P < 0.02) for each degree Celsius increase in maximum daily temperature, and increased by 0.15 L AU−1 (P < 0.06) as cattle grew and the season progressed. Relative humidity is shown to be an important determinant of WC in grazing cattle. Key words: Water consumption, animal unit, temperature, humidity, grazing cattle

Relationship between maximum daily temperature and mortality of broiler chickens during transport and lairage

2005 ◽  
Vol 46 (6) ◽  
pp. 647-651 ◽  
Author(s):  
P.D. Warriss ◽  
A. Pagazaurtundua ◽  
S.N. Brown
Keyword(s):  
Broiler Chickens ◽  
Daily Temperature ◽  
Maximum Daily Temperature
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