An alternative to classical latent class models selection methods for sparse binary data: An illustration with simulated data

• Autores: Carlomagno Araya Alpízar
• Localización: Revista de Matemática: Teoría y Aplicaciones, ISSN 2215-3373, ISSN-e 2215-3373, Vol. 23, Nº. 1, 2016, págs. 199-220
• Idioma: inglés
• DOI: 10.15517/rmta.v23i1.22448
• Títulos paralelos:
  • Un método alternativo para la selección de modelos de clases latentes en datos binarios escasos: Una ilustración con datos simulados
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• Resumen
  • español

    En el contexto de modelos de clases latentes con variables manifiestas binarias, se propone un método alternativo para resolver el problema de la estimación de la distribución empírica con tablas de contingencias escasas, donde la aproximación de los estadísticos de bondad de ajuste por la distribución Chi-Cuadrada no es válida. Se analiza datos binarios escasos, donde muchos patrones de respuesta que tienen frecuencias esperadas pequeñas, en conjuntos de datos con grados de datos escasos de 1 a 5, donde d = n/2p = n/R es un factor es mencionado en la literatura como determinante de la bondad de ajuste a la distribución Chi-Cuadrada. La propuesta presenta resultados válidos y confiables en las condiciones de los datos mencionadas. Para los resultados se presenta tasas de error tipo I para las pruebas tradiciones de bondad de ajuste. También se muestra que para niveles de densidad de datos d ≤ 5, el estadístico Pearson

  • English

    Within the context of a latent class model with manifest binary variables, we propose an alternative method that solves the problem of estimating empirical distribution with sparse contingency tables and the chi-square approximation for goodness-of-fit will not be valid. We analyze sparse binary data, where there are many response patterns with very small expected frequencies in several data sets varying in degree of sparseness from 1 to 5 defined d = n/2p = n/R is a factor that is mentioned in almost all prior literature as being an important determinant of how well the distribution is represented by the chi-squared.The proposed approach produced results that were valid and reliable under the mentioned problematic data conditions. Results from the proposal presented compare the rates of Type I for traditional goodness-of-fit tests. We also show that with data density d ≤ 5, Pearson’s statistic

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