El presente trabajo aborda el tema relacionado con el procesamiento estadístico de variables categóricas. Se explican los fundamentos matemáticos del análisis de Componentes Principales y del an´alisis de Regresión para datos categóricos. La unión de estas técnicas puede utilizarse para resolver problemas de clasificación.Debido a que estos son métodos relativamente nuevos, se decide utilizar otra técnica más conocida (´arboles de clasificación siguiendo criterios chi cuadrado) para realizar comparaciones de sus los resultados, con ayuda de la teoría de las curvas ROC. En la aplicación desarrollada se estudiaron pacientes supuestamente sanos del municipio de Santa Clara, Cuba, diagnosticados como hipertensos, prehipertensos y normotensos por un Comité de Expertos Médicos altamente calificados. La regresión categórica unida al análisis de Componentes Principales como m´etodo de selección de variables, resultó ser la mejor técnica ara resolver el problema de clasificación.
Climatological data with unreliable or missing values is an important area of
research, and multiple methods are available to fill in missing data and evaluate data
quality. Our study aims to compare the performance of different methods for estimating
missing values that are explicitly designed for precipitation and multipurpose
hydrological data. The climate variable used for the analysis was daily precipitation.
We considered two different climate and orographic regions to evaluate the effects of
altitude, precipitation regime and percentage of missing data on the Mean Absolute Error
of imputed values and using a homogeneity evaluation of meteorological stations. We
excluded from the analysis meteorological stations with more than 25% missing data. In
the semi-arid region, ReddPrec (optimal for 9 stations), and GCIDW (optimal for 8) were
the best performing methods for the 23 stations, with average MAE values of 1.63 mm/day
and 1.46 mm/day, respectively. In the humid region, GCIDW was optimal in ~59% of
stations, EM in ~24%, and ReddPrec in ~17%, with average MAE values of ~6.0 mm/day, 6.5
mm/day and ~9.8 mm/day, respectively. This research makes an important contribution to
identifying the most appropriate methods to impute daily precipitation in different
climatic regions of Mexico based on efficiency indicators and homogeneity
evaluation.
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