scholarly journals A Fisher scoring algorithm for the weighted regression method of QTL mapping

Heredity ◽  
2008 ◽  
Vol 101 (5) ◽  
pp. 453-464 ◽  
Author(s):  
L Han ◽  
S Xu
Keyword(s):  
Qtl Mapping ◽  
Regression Method ◽  
Weighted Regression ◽  
Fisher Scoring ◽  
Scoring Algorithm ◽  
Fisher Scoring Algorithm ◽  
Weighted Regression Method

scholarly journals Gram–Schmidt–Fisher scoring algorithm for parameter orthogonalization in MLE

2016 ◽  
Vol 3 (1) ◽  
pp. 1159847
Author(s):  
John Kwagyan ◽  
Victor Apprey ◽  
George E. Bonney ◽  
Zudi Lu
Keyword(s):  
Fisher Scoring ◽  
Scoring Algorithm ◽  
Fisher Scoring Algorithm

scholarly journals Weighted Regression Method for the Study of Pedestrian Flow Characteristics in Dhaka, Bangladesh

2013 ◽  
Vol 7 (4) ◽  
Author(s):  
Khalidur Rahman ◽  
Noraida Abdul Ghani ◽  
Anton Abdulbasah Kamil ◽  
Adli Mustafa
Keyword(s):  
Flow Characteristics ◽  
Regression Method ◽  
Weighted Regression ◽  
Pedestrian Flow ◽  
Weighted Regression Method

scholarly journals Geographically weighted regression: Method for analysing spatial nonstationarity of geographical phenomenon

Geografie ◽  
2008 ◽  
Vol 113 (2) ◽  
pp. 125-139 ◽  
Author(s):  
Pavlína Spurná
Keyword(s):  
Linear Regression ◽  
Geographically Weighted Regression ◽  
Quantitative Method ◽  
Regression Method ◽  
Weighted Regression ◽  
Parliamentary Election ◽  
Voting Behaviour ◽  
Significant Difference ◽  
Spatial Nonstationarity ◽  
Weighted Regression Method

The article deals with one of the new quantitative method used in geography, geographically weighted regression (GWR). This method is based on the premise that relationships between variables might not be constant across the study area and explores this phenomenon called spatial non-stationarity. Using the GWR technique to study voting behaviour in Czechia in the parliamentary election in 2002, it is evident that there is a significant difference between the linear regression and GWR models. The examples highlight the relevance and usefulness of GWR and show how it can improve geographical research and potentially also our understanding of geographical processes.

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