Common principal components model for symbolic data

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

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

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Symbolic Principal Components for Interval-valued Observations

One feature of contemporary datasets is that instead of the single point value in the p-dimensional space < seen in classical data, the data may take interval values thus producing hypercubes in <. This paper extends the methodology of classical principal components to that for interval-valued data. Two methods are proposed, viz., a vertices method which uses all the vertices of the observation...

متن کامل

Robust tests for the common principal components model

In multivariate analysis we often deal with situations involving several populations, such as discriminant analysis, where the assumption of equality of scatter matrices is usually assumed. Yet sometimes, this assumption is not adequate but problems related to an excessive number of parameters will arise if we estimate the scatter matrices separately for each population. In many practical situa...

متن کامل

Detecting influential observations in principal components and common principal components

Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis distance based on several robust estimates (l...

متن کامل

Functional common principal components models

In this paper, we discuss the extension to the functional setting of the common principal component model that has been widely studied when dealing with multivariate observations. We provide estimators of the common eigenfunctions and study their asymptotic behavior.

متن کامل

Common Functional Principal Components 1

Functional principal component analysis (FPCA) based on the Karhunen–Loève decomposition has been successfully applied in many applications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only by the theoretical challenge of this data situation, but also by the actual question of dynamics of...

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

ژورنال

عنوان ژورنال: Journal of the Japanese Society of Computational Statistics

سال: 2010

ISSN: 0915-2350,1881-1337

DOI: 10.5183/jjscs.23.1_41