Sankhya-Series B-Applied and Interdisciplinary Statistics最新文献

英文中文

Optimum Plans for Progressive Censored Competing Risk Data Under Kies Distribution ky分布下渐进式审查竞争风险数据的最优方案

Q4 STATISTICS & PROBABILITY

Sankhya-Series B-Applied and Interdisciplinary Statistics

Pub Date : 2023-10-21 DOI: 10.1007/s13571-023-00315-7

Prakash Chandra, Chandrakant Lodhi, Yogesh Mani Tripathi

引用次数: 0

Shrinkage Estimation of Location Parameter for Uniform Distribution Based on k-record Values 基于k记录值的均匀分布位置参数收缩估计

Q4 STATISTICS & PROBABILITY

Sankhya-Series B-Applied and Interdisciplinary Statistics

Pub Date : 2023-10-16 DOI: 10.1007/s13571-023-00313-9

Gajendra K. Vishwakarma, Shubham Gupta, A. M. Elsawah

引用次数: 0

Black-box optimization on hyper-rectangle using Recursive Modified Pattern Search and application to ROC-based Classification Problem 基于递归修正模式搜索的超矩形黑盒优化及其在roc分类问题中的应用

Q4 STATISTICS & PROBABILITY

Sankhya-Series B-Applied and Interdisciplinary Statistics

Pub Date : 2023-10-12 DOI: 10.1007/s13571-023-00312-w

Das, Priyam

In statistics, it is common to encounter multi-modal and non-smooth likelihood (or objective function) maximization problems, where the parameters have known upper and lower bounds. This paper proposes a novel derivative-free global optimization technique that can be used to solve those problems even when the objective function is not known explicitly or its derivatives are difficult or expensive to obtain. The technique is based on the pattern search algorithm, which has been shown to be effective for black-box optimization problems. The proposed algorithm works by iteratively generating new solutions from the current solution. The new solutions are generated by making movements along the coordinate axes of the constrained sample space. Before making a jump from the current solution to a new solution, the objective function is evaluated at several neighborhood points around the current solution. The best solution point is then chosen based on the objective function values at those points. Parallel threading can be used to make the algorithm more scalable. The performance of the proposed method is evaluated by optimizing up to 5000-dimensional multi-modal benchmark functions. The proposed algorithm is shown to be up to 40 and 368 times faster than genetic algorithm (GA) and simulated annealing (SA), respectively. The proposed method is also used to estimate the optimal biomarker combination from Alzheimer's disease data by maximizing the empirical estimates of the area under the receiver operating characteristic curve (AUC), outperforming the contextual popular alternative, known as step-down algorithm.

在统计学中，经常遇到多模态和非光滑似然(或目标函数)最大化问题，其中参数有已知的上界和下界。本文提出了一种新的无导数全局优化技术，可用于解决目标函数不明确或其导数难以或昂贵的问题。该技术基于模式搜索算法，该算法已被证明是有效的黑盒优化问题。该算法通过从当前解迭代生成新解来工作。新的解是通过沿着约束样本空间的坐标轴进行运动来生成的。在从当前解跳到新解之前，目标函数在当前解周围的几个邻域点上进行评估。然后根据这些点的目标函数值选择最佳解点。并行线程可以使算法更具可扩展性。通过优化多达5000维的多模态基准函数来评估该方法的性能。该算法比遗传算法(GA)和模拟退火算法(SA)分别快40倍和368倍。该方法还用于通过最大化接受者工作特征曲线(AUC)下面积的经验估计，从阿尔茨海默病数据中估计最佳生物标志物组合，优于上下文流行的替代方法，称为降压算法。

{"title":"Black-box optimization on hyper-rectangle using Recursive Modified Pattern Search and application to ROC-based Classification Problem","authors":"Das, Priyam","doi":"10.1007/s13571-023-00312-w","DOIUrl":"https://doi.org/10.1007/s13571-023-00312-w","url":null,"abstract":"In statistics, it is common to encounter multi-modal and non-smooth likelihood (or objective function) maximization problems, where the parameters have known upper and lower bounds. This paper proposes a novel derivative-free global optimization technique that can be used to solve those problems even when the objective function is not known explicitly or its derivatives are difficult or expensive to obtain. The technique is based on the pattern search algorithm, which has been shown to be effective for black-box optimization problems. The proposed algorithm works by iteratively generating new solutions from the current solution. The new solutions are generated by making movements along the coordinate axes of the constrained sample space. Before making a jump from the current solution to a new solution, the objective function is evaluated at several neighborhood points around the current solution. The best solution point is then chosen based on the objective function values at those points. Parallel threading can be used to make the algorithm more scalable. The performance of the proposed method is evaluated by optimizing up to 5000-dimensional multi-modal benchmark functions. The proposed algorithm is shown to be up to 40 and 368 times faster than genetic algorithm (GA) and simulated annealing (SA), respectively. The proposed method is also used to estimate the optimal biomarker combination from Alzheimer's disease data by maximizing the empirical estimates of the area under the receiver operating characteristic curve (AUC), outperforming the contextual popular alternative, known as step-down algorithm.","PeriodicalId":45608,"journal":{"name":"Sankhya-Series B-Applied and Interdisciplinary Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A General Equivalence Theorem for Crossover Designs under Generalized Linear Models 广义线性模型下交叉设计的一般等价定理

Q4 STATISTICS & PROBABILITY

Sankhya-Series B-Applied and Interdisciplinary Statistics

Pub Date : 2023-10-07 DOI: 10.1007/s13571-023-00314-8

Jeevan Jankar, Jie Yang, Abhyuday Mandal

引用次数: 0

Within Groups Designs: Inferences Based on A Robust Nonparametric Measure of Effect Size 组内设计:基于效应大小的稳健非参数测量的推论