Identifiability of multivariate logistic mixture models

نویسندگان

  • Ziqiang Shi
  • Tieran Zheng
  • Jiqing Han
چکیده

Mixture models have been widely used in modeling of continuous observations. For the possibility to estimate the parameters of a mixture model consistently on the basis of observations from the mixture, identifiability is a necessary condition. In this study, we give some results on the identifiability of multivariate logistic mixture models.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Convergence of latent mixing measures in finite and infinite mixture models

We consider Wasserstein distances for assessing the convergence of latent discrete measures, which serve as mixing distributions in hierarchical and nonparametric mixture models. We clarify the relationships between Wasserstein distances of mixing distributions and f -divergence functionals such as Hellinger and Kullback-Leibler distances on the space of mixture distributions using various iden...

متن کامل

Convergence of Latent Mixing Measures in Finite and Infinite Mixture Models By

This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i.e., Wasserstein metrics). The relationship between Wasserstein distances on the space of mixing measures and f -divergence functionals such as Hellinger and Kullback–Leibler distances on the space of mixture distributions is investigated in detail...

متن کامل

Convergence of latent mixing measures in nonparametric and mixture models

We consider Wasserstein distance functionals for assessing the convergence of latent discrete measures, which serve as mixing distributions in hierarchical and nonparametric mixture models. We clarify the relationships between Wasserstein distances of mixing distributions and f -divergence functionals such as Hellinger and Kullback-Leibler distances on the space of mixture distributions using v...

متن کامل

Mixtures of location-shifted symmetric distributions

This paper considers a nonparametric approach to fitting mixture distributions that assumes only that the components are symmetric and come from the same location family. Unlike some other nonparametric treatments of mixtures in the literature, our approach assumes univariate rather than multivariate observations. We discuss sufficient conditions for the identifiability of these mixture models,...

متن کامل

Granger Causality Networks for Categorical Time Series

We present two model-based methods for learning Granger causality networks for multivariate categorical time series. Our first proposal is based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex objective, non-identifiability, and presence of many local optima. To circumvent these problems, we recast inference in the MTD as a convex problem. The ne...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1208.3546  شماره 

صفحات  -

تاریخ انتشار 2012