Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in pharmaceutical industry. An efficient solution to this problem will help reduce the expenditure at the early stages of drug discovery. In an attemp to solve this problem, Mandal, Wu and Johnson (2006) proposed SELC algorithm which was motivated by SEL algorithm of Wu, Mao and Ma (1990). However, SELC fails to extract substantial information from the data to guide the search efficiently as this methodology is not based on any statistical modeling of the data.
The nature of fMRI studies impels the need for multi-objective designs that simultaneously accomplish statistical goals, circumvent psychological confounds, and fulfill customized requirements. Incorporating knowledge about fMRI designs, we propose an efficient algorithm to search for optimal multi-objective designs. This algorithm significantly outperforms previous search algorithms in terms of achieved efficiency, computation time and convergence rate. Furthermore, our design criterion allows fair, consistent design comparisons.
We consider the problem of estimating the Hurst parameter for long-range dependent processes using wavelets. Wavelet techniques have shown to effectively exploit the asymptotic linear relationship that forms the basis of constructing an estimator. However, it has been noticed that the commonly adopted standard wavelet estimator is vulnerable to various non-stationary phenomena that increasingly occur in practice and thus leads to unreliable results.
In this paper, we conduct an investigation of the null hypothesis distribution for functional magnetic resonance imaging (fMRI) data using multiscale analysis. Most current approaches to the analysis of fMRI data assume temporal independence, or, at best, simple models for temporal (short term or long term) dependence structure. The spatial structure of fMRI data is commonly assumed to be independent or weakly spatially dependent.
In this paper we examine rigorously the evidence for correlations among data size, transfer rate, and duration in Internet flows. We emphasize various statistical approaches for studying correlations, including computing Pearson's correlation coefficient and using the extremal dependence analysis (EDA) method. We apply these methods to three large data sets of packet traces from a diverse set of networks. Our major results show that correlation between size and duration is much weaker than one might expect.
In this article we proposed a data-driven method of generalized adaptive ridge (gar) for an automatic yet adaptive regression shrinkage and selection. We show that, in theory, gar can be equivalent to adaptive lasso, adaptive ridge regression and adaptive elastic net under appropriate conditions. Specifically, if the regression parameters truly enjoy a sparse representation, gar performs like the most recently proposed adaptive lasso (Zou, 2006), hence, is able to identify relevant predictors consistently.
We consider a general adaptive L2-regularized optimization problem ^ ¯(¸) = arg min¯;¤ `(y; ¯)+¸¯T¤W¯, where ` is a loss function, ¤ and W are two diagonal matrices. We show that with appropriate choice of ¸ and ¤, if ` is differentiable, then the above adaptive L2 penalty term is equivalent to adaptive L1 penalty, adaptive L2 penalty, and combined adaptive L1 and L2 penalty. Therefore, this method is a data-driven method, which automatically choose a penalty among the three penalty terms.
A model for cell adhesion mediated by dimeric binding was developed in a companion paper. To test the model, we used a micropipette adhesion frequency assay to measure dimeric E-selectin-Ig or monomeric soluble E-selectin (sE-selectin) interacting with dimeric P-selectin glycoprotein ligand 1 (PSGL-1) or monomeric soluble PSGL-1 (sPSGL-1). Higher adhesions were mediated by E-selectin-Ig interacting with PSGL-1 than sPSGL-1, whereas similar adhesions were mediated by sE-selectin interacting with PSGL-1 or sPSGL-1.
Functional magnetic resonance imaging (fMRI) is an important tool for scientists studying brain function. FMRI data are complex in nature: they are massive in size and a low signal-to-noise ratio makes the elimination of some noise prior to model fitting desirable for improved identification of true brain activity. We propose two methods of reducing this noise: generalized indicator functional analysis and a hidden Markov model.
The models used in small-area inference often involve unobservable random effects. While this can significantly improve the adaptivity and flexibility of a model, it also increases the variability of both point and interval estimators. If we could test for the existence of the random effects, and if the test were to show that they were unlikely to be present, then we would arguably not need to incorporate them into the model, and thus could significantly improve the precision of the methodology. In this paper we suggest an approach of this type.