Weight matrix models for signal sequence motif are simple. A main limitation of the models is the assumption of independence between positions. Signal enhancement is achieved by taking the total likelihood as the obje...Weight matrix models for signal sequence motif are simple. A main limitation of the models is the assumption of independence between positions. Signal enhancement is achieved by taking the total likelihood as the objective function for maximization to cluster sequences into groups with different patterns. As an example, the initial and terminal signals for translation in rice genome are examined.展开更多
In this paper,an approach to improving consistency of judgement matrix in the Analytic Hierarchy Process (AHP) is presented,which utilizes the eigenvector to revise a pair of entries of judgement matrix each time.By u...In this paper,an approach to improving consistency of judgement matrix in the Analytic Hierarchy Process (AHP) is presented,which utilizes the eigenvector to revise a pair of entries of judgement matrix each time.By using this method,any judgement matrix with a large C.R.can be modified to a matrix which can both tally with the consistency requirement and reserve the most information that the original matrix contains.An algorithm to derive a judgement matrix with acceptable consistency (i.e.,C.R.<0.1) and two criteria of evaluating modificatory effectiveness are also given.展开更多
For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting sc...For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations(GEE) model.Second,we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation.The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters.Moreover,we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified.For the selection of tuning parameter,we develop a consistent penalized quadratic form(PQF) function criterion.The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study.展开更多
基金the Special Funds for Major National Basic Research Projects，国家自然科学基金，Research Project 248 of Beijing
文摘Weight matrix models for signal sequence motif are simple. A main limitation of the models is the assumption of independence between positions. Signal enhancement is achieved by taking the total likelihood as the objective function for maximization to cluster sequences into groups with different patterns. As an example, the initial and terminal signals for translation in rice genome are examined.
基金This research is supported by the National Natural Science Foundation of China under Project 79970093, the Ph.D. Dissertation Foundation of Southeast University-NARI-Relays Electric Co. Ltd.
文摘In this paper,an approach to improving consistency of judgement matrix in the Analytic Hierarchy Process (AHP) is presented,which utilizes the eigenvector to revise a pair of entries of judgement matrix each time.By using this method,any judgement matrix with a large C.R.can be modified to a matrix which can both tally with the consistency requirement and reserve the most information that the original matrix contains.An algorithm to derive a judgement matrix with acceptable consistency (i.e.,C.R.<0.1) and two criteria of evaluating modificatory effectiveness are also given.
基金supported by National Natural Science Foundation of China(Grant No.11201306)the Innovation Program of Shanghai Municipal Education Commission(Grant No.13YZ065)+2 种基金the Fundamental Research Project of Shanghai Normal University(Grant No.SK201207)the scholarship under the State Scholarship Fund by the China Scholarship Council in 2011the Research Grant Council of Hong Kong, Hong Kong,China(Grant No.#HKBU2028/10P)
文摘For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations(GEE) model.Second,we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation.The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters.Moreover,we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified.For the selection of tuning parameter,we develop a consistent penalized quadratic form(PQF) function criterion.The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study.
