Online learning for quantile regression and support vector regression

We consider for quantile regression and support vector regression a kernel-based online learning algorithm associated with a sequence of insensitive pinball loss functions. Our error analysis and derived learning rates show quantitatively that the statistical performance of the learning algorithm may vary with the quantile parameter τ$\tau$. In our analysis we overcome the technical difficulty caused by the varying insensitive parameter introduced with a motivation of sparsity.     

Chebyshev's inequality for Hilbert-space valued random elements with estimated mean and covariance

We obtain a generalization of the Chebyshev's inequality for random elements taking values in a separable Hilbert space with estimated mean and covariance.     

Bayesian classification of tumours by using gene expression data

Summary.  Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classi-fication of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification methods based on reproducing kernel Hilbert spaces for the analysis of microarray data. We consider the logistic likelihood as well as likelihoods related to support vector machine models. It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter.     

Functional analysis of variance for Hilbert-valued multivariate fixed effect models

This paper presents new results on functional analysis of variance for fixed effect models with correlated Hilbert-valued Gaussian error components. The geometry of the reproducing kernel Hilbert space of the error term is considered in the computation of the total sum of squares, the residual sum of squares, and the sum of squares due to the regression. Under suitable linear transformation of the correlated functional data, the distributional characteristics of these statistics, their moment generating and characteristic functions, are derived. Fixed effect linear hypothesis testing is finally formulated in the Hilbert-valued multivariate Gaussian context considered.     

Encoding dissimilarity data for statistical model building

We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A “newbie” algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a smoothing spline ANOVA penalized likelihood model, a support vector machine, or any model that will admit reproducing kernel Hilbert space components, for nonparametric regression, supervised learning, or semisupervised learning. Future work and open questions are discussed. The papers are:     

Best linear unbiased interpolation of order statistics

We derive the best linear unbiased interpolation for the missing order statistics of a random sample using the well-known projection theorem. The proposed interpolation method only needs the first two moments on both sides of a missing order statistic. A simulation study is performed to compare the proposed method with a few interpolation methods for exponential and Lévy distributions.     

广义希尔伯特变换及其数字实现

基于广义Hilbert变换将传统的Hilbert变换由整数阶向分数阶的推广,其应用领域也得到了扩展。首先,在频域定义广义Hilbert变换,利用广义Hilbert变换来构造新的广义解析信号。然后从数字信号处理角度来考察理想的广义数字Hilbert变换器的基本性质及其数字实现。文中利用窗函数法和频率采样法设计了FIR广义数字Hilbert变换器,并分析了设计误差。     

一个新的Hilbert型不等式

通过估算权函数，引入参数，建立一种新的含参数且具有最佳常数因子的Hilbert型积分不等式，并给出了两种最佳推广及其相应的等价式．