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Changlin Mei · Study

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Changlin Mei

6 works· latest 2021· 3 institutionscluster: provisionalORCID 0000-0002-4663-7993

h-index6

i106

Citations347

Works here6

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Most cited in this library

Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model2006 · 89 citations
Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique2008 · 73 citations
Non-pharmacological interventions for improving sleep quality in patients on dialysis: systematic review and meta-analysis2014 · 63 citations
A bootstrap test for constant coefficients in geographically weighted regression models2016 · 57 citations
Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing2021 · 51 citations

Works

6 shown

Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing

International Journal of Geographical Information Systems·2021·7 authors

51citations

A bootstrap test for constant coefficients in geographically weighted regression models

International Journal of Geographical Information Systems·2016·2 authors·Open access

57citations

Non-pharmacological interventions for improving sleep quality in patients on dialysis: systematic review and meta-analysis

Sleep Medicine Reviews·2014·8 authors·review

63citations

Scientific Publications in Nephrology and Urology Journals from Chinese Authors in East Asia: A 10-Year Survey of the Literature

PLoS ONE·2011·15 authors·Open access

14citations

Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique

Environment and Planning A Economy and Space·2008·3 authors

73citations

Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model

Environment and Planning A Economy and Space·2006·3 authors

89citations