Nonparametric Growth Curve Model with Local Linear Approximation

The growth curve model has been developed for longitudinal data, and its time trend is usually described by polynomials. However, it is difficult to interpret each coefficient of the polynomials with higher degrees, even when the number of repetitions is sufficiently large. We propose herein an alternative growth curve model having time-varying coefficients.     

Variable selection via the weighted group lasso for factor analysis models

We consider the problem of selecting variables in factor analysis models. The $L_1$ regularization procedure is introduced to perform an automatic variable selection. In the factor analysis model, each variable is controlled by multiple factors when there are more than one underlying factor. We treat parameters corresponding to the multiple factors as grouped parameters, and then apply the group lasso. Furthermore, the weight of the group lasso penalty is modified to obtain appropriate estimates and improve the performance of variable selection. Crucial issues in this modeling procedure include the selection of the number of factors and a regularization parameter. Choosing these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating the factor analysis model via the weighted group lasso. Monte Carlo simulations are conducted to investigate the effectiveness of the proposed procedure. A real data example is also given to illustrate our procedure. The Canadian Journal of Statistics 40: 345–361; 2012 © 2012 Statistical Society of Canada     

Creating facial animation of characters via MoCap data

We consider the problem of generating 3D facial animation of characters. An efficient procedure is realized by using the motion capture data (MoCap data), which is obtained by tracking the facial markers from an actor/actress. In some cases of artistic animation, the MoCap actor/actress and the 3D character facial animation show different expressions. For example, from the original facial MoCap data of speaking, a user would like to create the character facial animation of speaking with a smirk. In this paper, we propose a new easy-to-use system for making character facial animation via MoCap data. Our system is based on the interpolation: once the character facial expressions of the starting and the ending frames are given, the intermediate frames are automatically generated by information from the MoCap data. The interpolation procedure consists of three stages. First, the time axis of animation is divided into several intervals by the fused lasso signal approximator. In the second stage, we use the kernel k-means clustering to obtain control points. Finally, the interpolation is realized by using the control points. The user can easily create a wide variety of 3D character facial expressions by changing the control points.     

The Risk Screening for Indoor Air Pollution Chemicals in Japan

In recent years, public health problems caused by indoor air pollution have been drawing strong public concern in Japan. After conducting extensive exposure assessment, governmental agencies have taken effective measures to solve the problem; for instance, "Guidelines for indoor air quality (IAQ)" of 13 chemicals, for example, formaldehyde, toluene, and xylene, has been established. Thousands of chemicals have been identified in the indoor environment. Priority rating of those chemicals, however, was not based on the health risk level. We developed a risk-screening scheme for indoor air pollution chemicals and analyzed the current status of the risk levels of those chemicals in Japan. We researched scientific knowledge of health hazards and exposure surveys of indoor air pollution chemicals in Japan, and classified those chemicals based on the health risk level estimated from the scheme. The risk levels of 93 chemicals were characterized and six chemicals (formaldehyde, acrolein, 1,4-dichlorobenzene, benzene, tetrachloroethylene, and benzo(a)pyrene) were classified in the highest risk category.