Automated discovery of test statistics using genetic programming

被引：2

作者：

Moore, Jason H. ^{[1
]}

Olson, Randal S. ^{[1
]}

Chen, Yong ^{[1
]}

Sipper, Moshe ^{[1
,2
]}

机构：

[1] Univ Penn, Inst Biomed Informat, Perelman Sch Med, Philadelphia, PA 19104 USA

[2] Ben Gurion Univ Negev, Dept Comp Sci, IL-84105 Beer Sheva, Israel

来源：

GENETIC PROGRAMMING AND EVOLVABLE MACHINES | 2019年 / 20卷 / 01期

基金：

美国国家卫生研究院;

关键词：

Genetic programming; Statistics; Optimization; t test;

D O I：

10.1007/s10710-018-9338-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about the desirable properties of a good test statistic in addition to having an engine that can develop and explore candidate mathematical solutions with an intuitive representation. In this paper we describe a genetic programming-based system for the automated discovery of new test statistics. Specifically, our system was able to discover test statistics as powerful as the t test for comparing sample means from two distributions with equal variances.

引用

页码：127 / 137

页数：11

共 13 条

[1]

Casella G, 2021, STAT INFERENCE

[2] Semiparametric Tests for Identifying Differentially Methylated Loci With Case-Control Designs Using Illumina Arrays [J].

Chen, Yong ;

Ning, Yang ;

Hong, Chuan ;

Wang, Shuang .

GENETIC EPIDEMIOLOGY, 2014, 38 (01) :42-50

[3] DESIGN OF EXPERIMENTS AND REGRESSION [J].

COX, DR .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 1984, 147 :306-315

[4] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].

Deb, K ;

Pratap, A ;

Agarwal, S ;

Meyarivan, T .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197

[5]

Fortin FA, 2012, J MACH LEARN RES, V13, P2171

[6] Extensive variation and low heritability of DNA methylation identified in a twin study [J].

Gervin, Kristina ;

Hammero, Martin ;

Akselsen, Hanne E. ;

Moe, Rune ;

Nygard, Heidi ;

Brandt, Ingunn ;

Gjessing, Hakon K. ;

Harris, Jennifer R. ;

Undlien, Dag E. ;

Lyle, Robert .

GENOME RESEARCH, 2011, 21 (11) :1813-1821

[7] Increased methylation variation in epigenetic domains across cancer types [J].

Hansen, Kasper Daniel ;

Timp, Winston ;

Bravo, Hector Corrada ;

Sabunciyan, Sarven ;

Langmead, Benjamin ;

McDonald, Oliver G. ;

Wen, Bo ;

Wu, Hao ;

Liu, Yun ;

Diep, Dinh ;

Briem, Eirikur ;

Zhang, Kun ;

Irizarry, Rafael A. ;

Feinberg, Andrew P. .

NATURE GENETICS, 2011, 43 (08) :768-U77

[8] PLEMT: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalized Exponential Tilt Mixture Models [J].

Hong, Chuan ;

Ning, Yang ;

Wang, Shuang ;

Wu, Hao ;

Carroll, Raymond J. ;

Chen, Yong .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2017, 112 (520) :1393-1404

[9]

Koza J R., 1992, GENETIC PROGRAMMING

[10] A New Wave: A Dynamic Approach to Genetic Programming [J].

Medernach, David ;

Fitzgerald, Jeannie ;

Azad, R. Muhammad Atif ;

Ryan, Conor .

GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2016, :757-764

← 1 2 →