Unbiased estimates of burrowing owl populations (Athene cunicularia) are essential to achieving diverse management and conservation objectives. We conducted visibility trials and developed logistic regression models to identify and correct for visibility bias associated with single, vehicle-based, visual survey occasions of breeding male owls during daylight hours in an agricultural landscape i...

PURPOSE To evaluate the minimum number of visual field (VF) tests required to precisely predict future VF results using ordinary least squares linear regression (OLSLR), quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model. METHODS Series of 15 VFs (Humphrey Field Analyzer 24-2 SITA standard) were analyzed from 247 eyes of 155 open-angle g...

New feature selection algorithms for linear threshold functions are described which combine backward elimination with an adaptive regularization method. This makes them particularly suitable to the classification of microarray expression data, where the goal is to obtain accurate rules depending on few genes only. Our algorithms are fast and easy to implement, since they center on an incrementa...

Texture mapping may give the impression of geometric details in a model using an image. But texture always was captured under special light condition. If the lighting in virtual environment is different from the texture image, the result of rendering will be incorrect and unrealistic. This paper proposes an image-based method that requires basic texture map and coefficient maps to interpolate l...

Marketing problems often involve binary classification of customers into “buyers” versus “non-buyers” or “prefers brand A” versus “prefers brand B”. These cases require binary classification models such as logistic regression, linear, and quadratic discriminant analysis. A promising recent technique for the binary classification problem is the Support Vector Machine (Vapnik (1995)), which has a...

This paper concerns a method of selecting the best subset of explanatory variables for a linear regression model. Employing Mallows’ Cp as a goodness-of-fit measure, we formulate the subset selection problem as a mixed integer quadratic programming problem. Computational results demonstrate that our method provides the best subset of variables in a few seconds when the number of candidate expla...

Airspace capacity estimates are important for managing air traffic and predicting the effectiveness of new airspace designs and proposed decision support tools. Because air traffic management relies on manual procedures, controller workload determines the traffic limit of most sectors. Current operational procedures for estimating capacity in United States airspace do not account for conflict a...

We revisit the problem of assortment optimization under the multinomial logit choice model with general constraints and propose new efficient optimization algorithms. Our algorithms do not make any assumptions on the structure of the feasible sets and in turn do not require a compact representation of constraints describing them. For the case of cardinality constraints, we specialize our algori...

In this article, we develop a general method for testing threshold effects in regression models, using sup-likelihood-ratio (LR)-type statistics. Although the sup-LR-type test statistic has been considered in the literature, our method for establishing the asymptotic null distribution is new and nonstandard. The standard approach in the literature for obtaining the asymptotic null distribution ...

We propose a novel feature selection method based on quadratic mutual information which has its roots in Cauchy–Schwarz divergence and Renyi entropy. The method uses the direct estimation of quadratic mutual information from data samples using Gaussian kernel functions, and can detect second order non-linear relations. Its main advantages are: (i) unified analysis of discrete and continuous dat...