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The interplay of optimality and combinatorics in experimental design

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TLDR
The design of statistical experiments, as developed by R. A. Fisher and his followers, often used combinatorial structures that yielded simple calculation of estimates and/or symmetric variances and covariances as mentioned in this paper.
Abstract
The design of statistical experiments, as developed by R. A. Fisher and his followers, often used combinatorial structures that yielded simple calculation of estimates and/or symmetric variances and covariances. Examples are block designs with balance, regression experiments with equally spaced observations, etc. More recently, considerations of optimality (choosing a design that achieves most accurate inference in some sense) have sometimes justified the traditional designs, but have sometimes led to new combinatorial investigations. Illustrations are given.

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Experimental Design: Review and Comment

TL;DR: In this article, the authors review major developments in the design of experiments, offer their thoughts on important directions for the future, and make specific recommendations for experimenters and statisticians who are students and teachers of experimental design.
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Bootstrap confidence interval estimates of cpk: an introduction

TL;DR: The interpretation of Cpk:, a common measure of process capability and confidence limits for it, is based on the assumption that the process is normally distributed as discussed by the authors, and the non-parametric but computer intensive method called Bootstrap is introduced and three Bootstrap confidence interval estimates for C^ are defined.
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A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models

TL;DR: A new multi-objective evolutionary algorithm-based ensemble optimizer coupled with neural network models for undertaking feature selection and classification problems and the outcome positively demonstrates that the proposed MmGA-basedsemble optimizer is able to improve the classification performances of Neural network models with a smaller number of input features.
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Objective detection of evoked potentials using a bootstrap technique.

TL;DR: The bootstrap method is proposed, which is based on randomly resampling the original data and gives an estimate of the probability that the response obtained is due to random variation in the data rather than a physiological response.
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The potential use of artificially produced monozygotic twins for comparative experiments.

TL;DR: The uniformity of twins has been examined by assembling estimates of the intraclass correlation coefficient (rho(I)) available in the literature for a variety of parameters studied in cattle monozygotic twins and human dizygotic and monozygosis twins.
References
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Journal ArticleDOI

The Equivalence of Two Extremum Problems

TL;DR: In this article, the authors consider the problem of defining probability measures with finite support, i.e., measures that assign probability one to a set consisting of a finite number of points.
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Optimum Allocation in Linear Regression Theory

TL;DR: For the estimation of a single quantity of form, the optimum allocation comprises two or three sources as discussed by the authors, and the corresponding number is 2 or 3 for estimation of both parameters, the best proportions are indicated in Sections 2 and 4 below.
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Optimality of Certain Asymmetrical Experimental Designs

TL;DR: In this paper, the problem of finding an optimal design for the elimination of one-way heterogeneity when a balanced block design does not exist is studied, and a general result on the optimality of certain asymmetrical designs is proved and applied to the block design setting.
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