Notice of Retraction: Investigating the relationship among extreme climate indices by a varying-coefficient regression model

The changing frequency of extreme climate events generally has profound impacts on our living environment and decision-makers. Based on the daily temperature and precipitation data collected from 753 stations in China during 1961–2005, the geographically weighted regression (GWR) model is used to investigate the relationship between the index of frequency of extreme precipitation (FEP) and other climate extreme indices including frequency of warm days (FWD), frequency of warm nights (FWN), frequency of cold days (FCD), and frequency of cold nights (FCN). Assisted by some statistical tests, it is found that the regression relationship has significant spatial nonstationarity and the influence of each explanatory variable (namely, FWD, FWN, FCD, and FCN) on FEP also exhibits significant spatial inconsistency. Furthermore, some meaningful regional characteristics for the relationship between the studied extreme climate indices are obtained.

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

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.1398 ◽

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.

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Estimation of the error distribution in a varying coefficient regression model

Journal of Nonparametric Statistics ◽

10.1080/10485252.2018.1429608 ◽

2018 ◽

Vol 30 (2) ◽

pp. 392-429 ◽

Cited By ~ 1

Author(s):

Anton Schick ◽

Yilin Zhu ◽

Xiaojie Du

Keyword(s):

Regression Model ◽

Error Distribution ◽

Varying Coefficient ◽

Varying Coefficient Regression

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Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

Iraqi Journal of Economic Sciences ◽

10.31272/ijes2020.66.6 ◽

2020 ◽

Vol 2020 (66) ◽

pp. 101-110

Author(s):

. Azhar Kadhim Jbarah ◽

Prof Dr. Ahmed Shaker Mohammed

Keyword(s):

Regression Model ◽

Moving Average ◽

Estimation Method ◽

Estimation Methods ◽

Autoregressive Moving Average ◽

Moving Average Models ◽

Error Terms ◽

Relationship Of ◽

The Relationship ◽

Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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