In a survival analysis context, we suggest a new method to estimate the piecewise constant hazard rate model. The method provides an automatic procedure to find the number and location of cut points and to estimate th...In a survival analysis context, we suggest a new method to estimate the piecewise constant hazard rate model. The method provides an automatic procedure to find the number and location of cut points and to estimate the hazard on each cut interval. Estimation is performed through a penalized likelihood using an adaptive ridge procedure. A bootstrap procedure is proposed in order to derive valid statistical inference taking both into account the variability of the estimate and the variability in the choice of the cut points. The new method is applied both to simulated data and to the Mayo Clinic trial on primary biliary cirrhosis. The algorithm implementation is seen to work well and to be of practical relevance.展开更多
To characterize the clutter spectrum center-shift and spread of airborne radar caused by the platform motion, a novel Doppler Distributed Clutter (DDC) model is proposed to describe the clutter covariance matrix in te...To characterize the clutter spectrum center-shift and spread of airborne radar caused by the platform motion, a novel Doppler Distributed Clutter (DDC) model is proposed to describe the clutter covariance matrix in temporal domain. Based on this parametric model, maximum likelihood, subspace based method and other super- resolution methods are introduced into the Doppler parameters estimation, and more excellent performance is obtained than with the conventional approaches in frequency domain. The theoretical derivation and real experimental results are also provided to validate this novel model and methods of parameter estimating.展开更多
The problem of estimating high-dimensional Gaussian graphical models has gained much attention in recent years. Most existing methods can be considered as one-step approaches, being either regression-based or likeliho...The problem of estimating high-dimensional Gaussian graphical models has gained much attention in recent years. Most existing methods can be considered as one-step approaches, being either regression-based or likelihood-based. In this paper, we propose a two-step method for estimating the high-dimensional Gaussian graphical model. Specifically, the first step serves as a screening step, in which many entries of the concentration matrix are identified as zeros and thus removed from further consideration. Then in the second step, we focus on the remaining entries of the concentration matrix and perform selection and estimation for nonzero entries of the concentration matrix. Since the dimension of the parameter space is effectively reduced by the screening step,the estimation accuracy of the estimated concentration matrix can be potentially improved. We show that the proposed method enjoys desirable asymptotic properties. Numerical comparisons of the proposed method with several existing methods indicate that the proposed method works well. We also apply the proposed method to a breast cancer microarray data set and obtain some biologically meaningful results.展开更多
文摘In a survival analysis context, we suggest a new method to estimate the piecewise constant hazard rate model. The method provides an automatic procedure to find the number and location of cut points and to estimate the hazard on each cut interval. Estimation is performed through a penalized likelihood using an adaptive ridge procedure. A bootstrap procedure is proposed in order to derive valid statistical inference taking both into account the variability of the estimate and the variability in the choice of the cut points. The new method is applied both to simulated data and to the Mayo Clinic trial on primary biliary cirrhosis. The algorithm implementation is seen to work well and to be of practical relevance.
基金Supported by the National Natural Science Foundation of China(71803001)Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province(gxyqZD2019031)the Social Science Foundation of China(15CTJ008)。
基金Supported by the Fundamental Research Funds for the Central Universities and the National Natural Science Foundation of China(11601500,11671374,11771418)。
文摘To characterize the clutter spectrum center-shift and spread of airborne radar caused by the platform motion, a novel Doppler Distributed Clutter (DDC) model is proposed to describe the clutter covariance matrix in temporal domain. Based on this parametric model, maximum likelihood, subspace based method and other super- resolution methods are introduced into the Doppler parameters estimation, and more excellent performance is obtained than with the conventional approaches in frequency domain. The theoretical derivation and real experimental results are also provided to validate this novel model and methods of parameter estimating.
基金National Natural Science Foundation of China (Grant No. 11671059)。
文摘The problem of estimating high-dimensional Gaussian graphical models has gained much attention in recent years. Most existing methods can be considered as one-step approaches, being either regression-based or likelihood-based. In this paper, we propose a two-step method for estimating the high-dimensional Gaussian graphical model. Specifically, the first step serves as a screening step, in which many entries of the concentration matrix are identified as zeros and thus removed from further consideration. Then in the second step, we focus on the remaining entries of the concentration matrix and perform selection and estimation for nonzero entries of the concentration matrix. Since the dimension of the parameter space is effectively reduced by the screening step,the estimation accuracy of the estimated concentration matrix can be potentially improved. We show that the proposed method enjoys desirable asymptotic properties. Numerical comparisons of the proposed method with several existing methods indicate that the proposed method works well. We also apply the proposed method to a breast cancer microarray data set and obtain some biologically meaningful results.
