Fumitake Sakaori

Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

There is currently much discussion about lasso-type regularized regression which is a useful tool for simultaneous estimation and variable selection. Although the lasso-type regularization has several advantages in regression modelling, owing to its sparsity, it suffers from outliers because of using penalized least-squares methods. To overcome this issue, we propose a robust lasso-type estimation procedure that uses the robust criteria as the loss function, imposing L1-type penalty called the elastic net. We also introduce to use the efficient bootstrap information criteria for choosing optimal regularization parameters and a constant in outlier detection. Simulation studies and real data analysis are given to examine the efficiency of the proposed robust sparse regression modelling. We observe that our modelling strategy performs well in the presence of outliers.

PERMUTATION TEST FOR EQUALITY OF CORRELATION COEFFICIENTS IN TWO POPULATIONS

ABSTRACT The purpose of this paper is to investigate the permutation tests for equality of correlation coefficients among two independent populations. We discuss how to apply permutation test to this problem and its asymptotic suitability. We also show some simulation studies and an example of the Iris data.

Correlation analysis of principal components from two populations

We investigate a correlation coefficient of principal components from two sets of variables. Using perturbation expansion, we get a limiting distribution of the correlation. In addition, we obtain a limiting distribution of the Fishers z transformation of the above correlation. Additionally, we verify the accuracy of the limiting distributions using Monte Carlo simulations. Finally in this study, we present two examples and a bootstrap estimation.

Typing Attitudes Toward Exercise and Investigating Motivation Suitable for TPO

In recent years, lifestyle diseases have become a serious problem in Japan. According to a survey by the Ministry of Health, Labour and Welfare, more than half the causes of death in FY 2005 were attributed to lifestyle diseases. To prevent lifestyle diseases, diet improvements and regular exercise are necessary. However, many people cannot continue exercise. In this research, we classified the sports consciousness of participants, measured their motivation during exercise in various TPOs with questionnaires, and clarified these relationships. We also considered the method of motivation for exercise.

Forecasting Symbolic Candle Chart-Valued Time Series

This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries’. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.