Ir al contenido

Documat


Kernels, degrees of freedom, and power properties of quadratic distance goodness-of-fit tests

  • Autores: Bruce G. Lindsay, Marianthi Markatou, Surajit Ray
  • Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 109, Nº 505, 2014, págs. 395-410
  • Idioma: inglés
  • DOI: 10.1080/01621459.2013.836972
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for constructing optimal kernels for specific alternatives. We then introduce a midpower analysis as a device for choosing optimal degrees of freedom for a family of alternatives of interest. Finally, we introduce a new diffusion kernel, called the Pearson-normal kernel, and study the extent to which the normal approximation to the power of tests based on this kernel is valid. Supplementary materials for this article are available online.


Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno