Kinesthetic Illusion of Being Pulled Sensation Enables Haptic Navigation for Broad Social Applications

Many handheld force-feedback devices have been proposed to provide a rich experience with mobile devices. However, previously reported devices have been unable to generate both constant and translational force. They can only generate transient rotational force since they use a change in angular momentum. Here, we exploit the nonlinearity of human perception to generate both constant and translational force. Specifically, a strong acceleration is generated for a very brief period in the desired direction, while a weaker acceleration is generated over a longer period in the opposite direction. The internal human haptic sensors do not detect the weaker acceleration, so the original position of the mass is \"washed out\". The result is that the user is tricked into perceiving a unidirectional force. This force can be made continuous by repeating the motions. This chapter describes the pseudoattraction force technique, which is a new force feedback technique that enables mobile devices to create a the sensation of two-dimensional force. A prototype was fabricated in which four slider-crank mechanism pairs were arranged in a cross shape and embedded in a force feedback display. Each slider-crank mechanism generates a force vector. By using the sum of the generated vectors, which are linearly independent, the force feedback display can create a force sensation in any arbitrary direction on a two-dimensional plane. We also introduce an interactive application with the force feedback display, an interactive robot, and a vision-based positioning system

Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity

This paper provides methods for flexibly capturing unobservable heterogeneity from longitudinal data in the context of an exponential family of distributions. The group memberships of individual units are left unspecified, and their heterogeneity is influenced by group-specific unobservable structures, as well as heterogeneous regression coefficients. We discuss a computationally efficient estimation method and derive the corresponding asymptotic theory. The established asymptotic theory includes verifying the uniform consistency of the estimated group membership. To test the heterogeneous regression coefficients within groups, we propose the Swamy-type test, which considers unobserved heterogeneity. We apply the proposed method to study the market structure of the taxi industry in New York City. Our method reveals interesting important insights from large-scale longitudinal data that consist of over 450 million data points

Panel data models with grouped factor structure under unknown group membership

This paper studies panel data models with unobserved group factor structures. The group membership of each unit and the number of groups are left unspecified. The number of explanatory variables can be large. We estimate the model by minimizing the sum of least squared errors with a shrinkage penalty. The regressions coefficients can be homogeneous or group specific. The consistency and asymptotic normality of the estimator are established. We also introduce new $C_p$-type criteria for selecting the number of groups, the numbers of group-specific common factors and relevant regressors. Monte Carlo results show that the proposed method works well. We apply the method to the study of US mutual fund returns under homogeneous regression coefficients, and the China mainland stock market under group-specific regression coefficients

Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors

This paper analyzes multifactor models in the presence of a large number of potential observable risk factors and unobservable common and group-specific pervasive factors. We show how relevant observable factors can be found from a large given set and how to determine the number of common and group-specific unobservable factors. The method allows consistent estimation of the beta coefficients in the presence of correlations between the observable and unobservable factors. The theory and method are applied to the study of asset returns for A-shares/B-shares traded on the Shanghai and Shenzhen stock exchanges, and to the study of risk prices in the cross section of returns

Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity

This paper introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobservable heterogeneity of each of the financial time series based on sensitivity to explanatory variables and to the unobservable factor structure. In our model, the dimension of the common factor structure varies across quantiles, and the factor structure is allowed to be correlated with the explanatory variables. The proposed method allows for both cross-sectional and serial dependence, and heteroskedasticity, which are common in financial markets. We propose new estimation procedures for both frequentist and Bayesian frameworks. Consistency and asymptotic normality of the proposed estimator are established. We also propose a new model selection criterion for determining the number of common factors together with theoretical support. We apply the method to analyze the returns for over 6,000 international stocks from over 60 countries during the subprime crisis, European sovereign debt crisis, and subsequent period. The empirical analysis indicates that the common factor structure varies across quantiles. We find that the common factors for the quantiles and the common factors for the mean are different

Panel data models with grouped factor structure under unknown group membership

This paper studies panel data models with unobserved group factor structures. The group membership of each unit and the number of groups are left unspecified. The number of explanatory variables can be large. We estimate the model by minimizing the sum of least squared errors with a shrinkage penalty. The regressions coefficients can be homogeneous or group specific. The consistency and asymptotic normality of the estimator are established. We also introduce new $C_p$-type criteria for selecting the number of groups, the numbers of group-specific common factors and relevant regressors. Monte Carlo results show that the proposed method works well. We apply the method to the study of US mutual fund returns under homogeneous regression coefficients, and the China mainland stock market under group-specific regression coefficients

Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors

This paper analyzes multifactor models in the presence of a large number of potential observable risk factors and unobservable common and group-specific pervasive factors. We show how relevant observable factors can be found from a large given set and how to determine the number of common and group-specific unobservable factors. The method allows consistent estimation of the beta coefficients in the presence of correlations between the observable and unobservable factors. The theory and method are applied to the study of asset returns for A-shares/B-shares traded on the Shanghai and Shenzhen stock exchanges, and to the study of risk prices in the cross section of returns