Frequentist statistical methods, such as hypothesis testing, are standard practice in papers that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their test assumptions or controlling family-wise errors multiple group comparisons, among several other problems. Bayesian Data Analysis (BDA) addresses many of the previously mentioned s...

Abstract Microbial exopolysaccharides (EPS) provide a broad range of applications. Thus, there is an increasing interest in the production, characterization, and use EPS derived from various microorganisms. Extremophile polysaccharides have unique properties applications due to its structures. The importance synthesized by new bacterial strain, Alkalibacillus sp. w3, was highlighted this study....

Introduction Introduction Background Background – – Dynamic power dissipation Dynamic power dissipation – – Glitch reduction Glitch reduction – – Previous LP model Previous LP model Process Process-variation variation-resistant LP model resistant LP model – – Process variation Process variation – – Delay model Delay model – – LP model based on worst LP model based on worst-case timing case timi...

We study the empirical likelihood approach to construct confidence intervals for the optimal value and the optimality gap of a given solution, henceforth quantify the statistical uncertainty of sample average approximation, for optimization problems with expected value objectives and constraints where the underlying probability distributions are observed via limited data. This approach relies o...

Stochastic convex optimization, by which the objective is expectation of a random function, an important and widely used method with numerous applications in machine learning, statistics, operations research, other areas. We study complexity stochastic optimization given only statistical query (SQ) access to function. show that well-known popular first-order iterative methods can be implemented...

The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. Such a complex algorithmic structure grants superior ...