Index Numbers and Weighted Means

1952 ◽  
Vol 19 (1) ◽  
pp. 90
Author(s):  
Dudley J. Cowden ◽  
R. W. Pfouts
Keyword(s):  
1912 ◽  
Vol 46 (545) ◽  
pp. 302-307
Author(s):  
Raymond Pearl

2014 ◽  
Vol 22 (2) ◽  
pp. 281-302
Author(s):  
Marco Simonotti ◽  
Francesca Salvo ◽  
Marina Ciuna

2019 ◽  
Vol 20 (4) ◽  
pp. 386-409
Author(s):  
Elmar Spiegel ◽  
Thomas Kneib ◽  
Fabian Otto-Sobotka

Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.


1938 ◽  
Vol 6 (3) ◽  
pp. 282-306 ◽  
Author(s):  
Douglas E. Scates ◽  
Virginia Fauntleroy
Keyword(s):  

1966 ◽  
Vol 48 (1) ◽  
pp. 108 ◽  
Author(s):  
W. A. Chance
Keyword(s):  

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