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Yoshihiko Maesono

Bio: Yoshihiko Maesono is an academic researcher from Kyushu University. The author has contributed to research in topics: Estimator & Edgeworth series. The author has an hindex of 7, co-authored 46 publications receiving 168 citations. Previous affiliations of Yoshihiko Maesono include Chuo University & Australian National University.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the H-decomposition of a jackknife estimator of the variance of U-statistic was obtained and an Edgeworth expansion with remainder term o (n −1 2 ) was established.

24 citations

Journal ArticleDOI
TL;DR: In this paper, distribution-free statistics are proposed for one-sample location test, and compared with the Wilcoxon signed rank test in terms of approximate Bahadur efficiency.
Abstract: Distribution-free statistics are proposed for one-sample location test, and are compared with the Wilcoxon signed rank test. It is shown that one of the statistics is superior to the Wilcoxon test in terms of approximate Bahadur efficiency. And we compare that statistic with the Wilcoxon test from the viewpoint of asymptotic expansion of power function under contiguous alternatives.

16 citations

Journal ArticleDOI
TL;DR: In this paper, an asymptotic ex pansion with remainder term o (N-1) was established for onesample U-statistics with kernel of arbitrary degree under some regularity conditions on kernel.
Abstract: Under some regularity conditions on kernel, an asymptotic ex pansion with remainder term o (N-1) is established for onesample U-statistics with kernel of arbitrary degree. This is an extension of the result by Callaert, Janssen and Veraverbeke [1].

16 citations

Journal ArticleDOI
TL;DR: In this paper, the authors obtained an asymptotic representation of the ratio statistic until the third order term, and discussed the mean squared error of the corrected estimators, including bias correction of the correlation coefficient and the sample coefficient of variation.
Abstract: Some statistics in common use take a form of a ratio of two statistics such as sample correlation coefficient, Pearson’s coefficient of variation and so on. In this paper, obtaining an asymptotic representation of the ratio statistic until the third order term, we will discuss asymptotic mean squared errors of the ratio statistics. We will also discuss bias correction of the sample correlation coefficient and the sample coefficient of variation. Mean squared errors of the corrected estimators are also obtained.

10 citations

Journal ArticleDOI
TL;DR: In this article, the asymptotic mean square errors of variance estimators for a class of U -statistics were investigated theoretically and the Edgeworth expansions of the estimators with remainder term o(n −1 ) were established.
Abstract: This paper studies variance estimators for a class of U -statistics. We obtain asymptotic representations of jackknife, Hinkley’s (1978) corrected jackknife, unbiased, Sen’s (1960) and new variance estimators. And we investigate asymptotic mean square errors of them, theoretically. The Edgeworth expansions of the estimators with remainder term o(n −1 ) are also established. We show that the normalized Hinkley’s corrected estimator coincides the normalized unbiased estimator until the order n −1/2 op(n −1 ).

8 citations


Cited by
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01 Jan 1997

892 citations

Journal ArticleDOI
01 Feb 2019
TL;DR: A new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems and results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.
Abstract: Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.

865 citations

Posted Content
TL;DR: This work proposes a novel social spider algorithm based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys, and introduces a new social animal foraging model into meta-heuristic design.
Abstract: The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The Social Spider Algorithm is evaluated by a series of widely-used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.

203 citations