Identification of errors-in-variables model with observation outliers based on Minimum-Covariance-Determinant
نویسندگان
چکیده
منابع مشابه
Bias-compensation based method for errors-in-variables model identification
It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by noise. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compnesation based method is proposed for identification of noisy input-output ...
متن کاملThe minimum weighted covariance determinant estimator
In this paper we introduce weighted estimators of the location and dispersion of a multivariate data set with weights based on the ranks of the Mahalanobis distances. We discuss some properties of the estimators like the breakdown point, influence function and asymptotic variance. The outlier detection capacities of different weight functions are compared. A simulation study is given to investi...
متن کاملmortality forecasting based on lee-carter model
over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...
15 صفحه اولOutlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator
The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...
متن کاملSome issues on Errors-in-Variables Identification
The Errors-in-Variables context is a challenging environment well known from many years that has seen an increasing amount of research and, consequently, of new results, only in relatively recent times. One of the appealing features of EIV models consists in their intrinsic capability of describing real processes and in relying only on limited sets of a–priori assumptions [24, 25]. These featur...
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: Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
سال: 2006
ISSN: 2188-4730,2188-4749
DOI: 10.5687/sss.2006.72