An upper bound on the Bayesian error bars for generalized linear regression

In the Bayesian framework, predictions for a regression problem are expressed in terms of a distribution of output values. The mode of this distribution corresponds to the most probable output, while the uncertainty associated with the predictions can conveniently be expressed in terms of error bars. In this paper we consider the evaluation of error bars in the context of the class of generalized linear regression models. We provide insights into the dependence of the error bars on the location of the data points and we derive an upper bound on the true error bars in terms of the contributions from individual data points which are themselves easily evaluated.

The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.

Generalized KPCA by adaptive rules in feature space

Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA). IRKPCA works well in dealing with outliers, and can be carried out in an iterative manner, which makes it suitable to process incremental input data. As in the traditional robust PCA (RPCA), a binary field is employed for characterizing the outlier process, and the optimization problem is formulated as maximizing marginal distribution of a Gibbs distribution. In this paper, this optimization problem is solved by stochastic gradient descent techniques. In IRKPCA, the outlier process is in a high-dimensional feature space, and therefore kernel trick is used. IRKPCA can be regarded as a kernelized version of RPCA and a robust form of kernel Hebbian algorithm. Experimental results on synthetic data demonstrate the effectiveness of IRKPCA. © 2010 Taylor & Francis.

Medical conditions and depressive, anxiety, and somatic symptoms in older adults with and without generalized anxiety disorder

OBJECTIVE: The objective of this study was to examine medical illness and anxiety, depressive, and somatic symptoms in older medical patients with generalized anxiety disorder (GAD). METHOD: A case-control study was designed and conducted in the University of California, San Diego (UCSD) Geriatrics Clinics. A total of fifty-four older medical patients with GAD and 54 matched controls participated. MEASUREMENTS: The measurements used for this study include: Brief Symptom Inventory-18, Mini International Neuropsychiatric Interview, and the Anxiety Disorders Interview Schedule. RESULTS: Older medical patients with GAD reported higher levels of somatic symptoms, anxiety, and depression than other older adults, as well as higher rates of diabetes and gastrointestinal conditions. In a multivariate model that included somatic symptoms, medical conditions, and depressive and anxiety symptoms, anxiety symptoms were the only significant predictors of GAD. CONCLUSION: These results suggest first, that older medical patients with GAD do not primarily express distress as somatic symptoms; second, that anxiety symptoms in geriatric patients should not be discounted as a byproduct of medical illness or depression; and third, that older adults with diabetes and gastrointestinal conditions may benefit from screening for anxiety.