Bayesian estimation of the number of principal components
نویسندگان
چکیده
منابع مشابه
Bayesian Estimation of Principal Components for Functional Data
Abstract. The area of principal components analysis (PCA) has seen relatively few contributions from the Bayesian school of inference. In this paper, we propose a Bayesian method for PCA in the case of functional data observed with error. We suggest modeling the covariance function by use of an approximate spectral decomposition, leading to easily interpretable parameters. We study in depth the...
متن کاملanalysis of reading comprehension needs of the students of paramedical studies: the case of the students of health information management (him)
چکیده ندارد.
15 صفحه اولthe effect of taftan pozzolan on the compressive strength of concrete in the environmental conditions of oman sea (chabahar port)
cement is an essential ingredient in the concrete buildings. for production of cement considerable amount of fossil fuel and electrical energy is consumed. on the other hand for generating one tone of portland cement, nearly one ton of carbon dioxide is released. it shows that 7 percent of the total released carbon dioxide in the world relates to the cement industry. considering ecological issu...
Varying the number of principal components for modeling sample structure
We examined the sensitivity of Coal-Map to the number of covariates used to model global and local sample structures. First, we represented the local sample structure using the top three covariates obtained after applying principal components analysis on the local partition Xl containing the test locus xj. Global sample structure was represented using the top two covariates after performing pri...
متن کاملSelecting the Number of Principal Components in Functional Data.
Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal Processing
سال: 2007
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2006.09.001