Background/Aims: There is a growing interest regarding the effect of differential misclassification on power and type I error rate in genome-wide association studies. We present an extension of a previously published test statistic: the likelihood ratio test allowing for errors ( LRT AE ). This test uses double-sample information on a subset of individuals to increase power for genetic associat...

This article describes the methods and sensitivity analyses used by the authors in an article published in the New England Journal of Medicine. Purchasers are experimenting with a variety of approaches to control health care costs, including limiting network contracts to lower-cost physicians and offering patients differential copayments to encourage them to visit "high-performance" (i.e., high...

Recently deep neural networks have shown many successful applications in different domains. For this lesion segmentation task, we utilize a deep convolutional neural network with 5 layers in a sliding window fashion to create a voxel-based classifier. We evaluate our system with Dice similarity, misclassification rate and area under the ROC curve. Based on experimental results our proposed CAD ...

In this project, we study and experiment with a category of classification algorithms that are practically effective in email spam filtering—online prediction. We devise layered algorithms that can potentially control the spam misclassification rate. We compare the results of using different feature vectors as input. Also, we present observations that some online algorithms are insensitive to t...

The authors present some examples to demonstrate that in certain nondifferential misclassification conditions with polychotomous exposure variables, estimates of odds ratios for categories at intermediate level of risk can be biased away from the null or can change direction. In addition, the authors present two examples to demonstrate that the slope of the dose-response trend for the true dist...

We carefully study how well minimizing convex surrogate loss functions corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors. We consider the agnostic setting, and investigate guarantees on the misclassification error of the loss-minimizer in terms of the margin error rate of the best predictor. We show that, aiming for such a...

We consider a special type of dimensionality reduction classification problem where the decision-making objective is to minimize misclassification cost and attributes (MMCA). We propose a two-stage solution approach for solving the MMCA problem. Using simulated data sets and different misclassification cost matrices, we test our two-stage exhaustive and simulated annealing procedures. Our resul...

Recursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations, However, validation based on more appropriate outof-sample observations indicates that lo...

This paper derives fundamental limits on the performance of compressive classification when the source is a mixture of Gaussians. It provides an asymptotic analysis of a Bhattacharya based upper bound on the misclassification probability for the optimal Maximum-A-Posteriori (MAP) classifier that depends on quantities that are dual to the concepts of diversity-order and coding gain in multi-ante...