Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is ...

Nowadays, automatic signature verification is an active area of researches in numerous applications such as bank check verification, access restriction or special areas such as police investigations. In our researches signature was captured by Topaz SigLite T-LBK750-HSB device, where some dynamic features of signature can be also registered. In many transactions, the electronic verification of ...

Imputation is one of the most common methods to reduce item non_response effects. Imputation results in a complete data set, and then it is possible to use naϊve estimators. After using most of common imputation methods, mean and total (imputation estimators) are still unbiased. However their variances (imputation variances) are underestimated by naϊve variance estimators. Sampling mechanism an...

One of the challenges posed by renewable energies is stabilization parameters related to quality electrical energy. This study demonstrates existence a relationship between input blocks hydropower and variation fundamental frequency electricity grid. By mapping production data provided Spanish Electric Network with measured in laboratory, it shown that gradients are correlated fluctuations for ...

Hiroshi Murata, Linda M. Zangwill, Yuri Fujino, Masato Matsuura, Atsuya Miki, Kazunori Hirasawa, Masaki Tanito, Shiro Mizoue, Kazuhiko Mori, Katsuyoshi Suzuki, Takehiro Yamashita, Kenji Kashiwagi, Nobuyuki Shoji, and Ryo Asaoka Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan Shiley Eye Institute Hamilton Glaucoma Center, University of California, San D...

Principal components analysis is traditionally presented as an interpretive multivariate technique, where the loadings are chosen to maximally explain the variance in the variable. However, we will consider it here mainly as a statistical learning tool, by using the derived components in a least squares regression to predict unobserved response variables using the principal components. Principa...

A new connectionist model for the solution of piecewise linear regression problems is introduced; it is able to reconstruct both continuous and non continuous real valued mappings starting from a finite set of possibly noisy samples. The approximating function can assume a different linear behavior in each region of an unknown polyhedral partition of the input domain. The proposed learning tech...

In this paper we consider a linear regression model with fixed design. A new rule for the selection of a relevant submodel is introduced on the basis of parameter tests. One particular feature of the rule is that subjective grading of the model complexity can be incorporated. We provide bounds for the mis-selection error. Simulations show that by using the proposed selection rule, the mis-selec...

This paper combines the least squaress estimate, least absolute deviation estimate, least median estimate with Bootstrap method. When the overall error distribution is unknown or it is not the normal distribution, we estimate the regression coefficient and confidence interval of coefficient, and through data simulation, obtain Bootstrap method, which can improve stability of regression coeffici...