Two practical issues in using LISCOMP for analysing continuous and ordered categorical variables

1999 ◽  
Vol 52 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Wai-Yin Poon ◽  
Sik-Yum Lee
Keyword(s):  
Categorical Variables ◽  
Ordered Categorical Variables ◽  
Ordered Categorical

Ranked set sampling for ordered categorical variables

2008 ◽  
Vol 36 (2) ◽  
pp. 179-191 ◽  
Author(s):  
Haiying Chen ◽  
Elizabeth A. Stasny ◽  
Douglas A. Wolfe
Keyword(s):  
Ranked Set Sampling ◽  
Categorical Variables ◽  
Ordered Categorical Variables ◽  
Ordered Categorical

scholarly journals Assessment of Climate Change in Italy by Variants of Ordered Correspondence Analysis

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 146-161
Author(s):  
Assuntina Cembalo ◽  
Rosaria Lombardo ◽  
Eric J. Beh ◽  
Gianpaolo Romano ◽  
Michele Ferrucci ◽  
...  
Keyword(s):  
Correspondence Analysis ◽  
Climate Changes ◽  
Southern Italy ◽  
Northern Italy ◽  
Meteorological Station ◽  
Categorical Variables ◽  
Bootstrap Sampling ◽  
Ordered Categorical Variables ◽  
Italian Regions ◽  
Ordered Categorical

This paper explores climate changes in Italy over the last 30 years. The data come from the European observation gridded dataset and are concerned with the temperature throughout the country. We focus our attention on two Italian regions (Lombardy in northern Italy and Campania in southern Italy) and on two particular years roughly thirty years apart (1986 and 2015). Our primary aim is to assess the most important changes in temperature in Italy using some variants of correspondence analysis for ordered categorical variables. Such variants are based on a decomposition method using orthogonal polynomials instead of singular vectors and allow one to easily classify the meteorological station observations. A simulation study, based on bootstrap sampling, is undertaken to demonstrate the reliability of the results.

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