Males of the noctuid moths, Heliothis virescens and H. subflexa locate mates based on species‐specific responses to female‐emitted pheromones that are composed of distinct blends of volatile compounds. We conducted genetic crosses between these two species and used AFLP marker‐based mapping of backcross families (H. subflexa direction) to determine which of the 30 autosomes in these moths contained quantitative trait loci (QTL) controlling the proportion of specific chemical components in the pheromone blends. Presence/absence of single H. virescens chromosomes accounted for 7–34% of the phenotypic variation among backcross females in seven pheromone components. For a set of three similar 16‐carbon acetates, two H. virescens chromosomes interacted in determining their relative amounts within the pheromone gland and together accounted for 53% of the phenotypic variance. Our results are discussed relative to theories about population genetic processes and biochemical mechanisms involved in the evolution of new sexual communication systems.

WANG, JIANGDIAN. Shape Restricted Nonparametric Regression with Bernstein Polynomials. (Under the direction of Sujit K. Ghosh.) There has been increasing interest in estimating a multivariate regression function subject to various shape restrictions, such as nonnegativity, isotonicity, convexity and concavity among many others. The estimation of such shape-restricted regression curves is more challenging for multivariate predictors, especially for functions with compact support. Most of the currently available statistical estimation methods for shape restricted regression functions are generally computationally very intensive. Some of the existing methods have perceptible boundary biases. This thesis considers a suitable class of univariate and multivariate Bernstein polynomials and proposes sieved estimators obtained from a nested sequence of shape-restricted multivariate Bernstein polynomials. The proposed nonparametric estimators are shown to be: (i) the regression function estimate is shown to be the solution of a quadratic programming problem; making it computationally attractive (ii) the nonparametric estimator is shown to be universally consistent under some mild regularity conditions and (iii) the estimation methodology is flexible in the sense that it can be easily adapted to accommodate many popular multivariate shape restrictions. Numerical results derived from simulated data sets and real data analysis are used to illustrate the superior performance of the proposed estimators compared to a few other existing estimators in terms of various goodness of fit metrics.

No abstract

No abstract