Comparison of Interval Analysis and Standard Statistical Methods for Estimating Experimental Data with Uncertainty

被引：0

作者：

Kumkov, S. I. ^{[1
,2
]}

Jaulin, L. ^{[3
]}

机构：

[1] Russian Acad Sci, Ural Branch, Krasovskii Inst Math & Mech, Ekaterinburg, Russia

[2] Ural Fed Univ, Ekaterinburg, Russia

[3] ENSTA Bretagne, Brest, France

来源：

MEASUREMENT TECHNIQUES | 2019年 / 62卷 / 02期

关键词：

interval analysis; statistical methods; noisy data; uncertainty conditions; process parameters; estimation;

D O I：

10.1007/s11018-019-01593-2

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Interval analysis procedures are used to estimate the parameters of an experimental chemical process under conditions of noise and uncertainty in the probabilistic characteristics of the measurement errors for a small measurement sample. Interval analysis makes it possible to describe exactly the set of admissible values of the estimated parameters needed for correct organization of the technological process through a correct choice of its parameters. Approximate estimates of the parameters are obtained by formal application of a statistical approach and it is shown that in this case the standard statistical approach yields essentially meaningless estimates for the parameters of the process studied here.

引用

页码：105 / 110

页数：6

共 38 条

[31] Design, comparison and application of artificial intelligence predictive models based on experimental data for estimating carbon dioxide concentration inside a building
Ballerini, Vincenzo
Valdiserri, Paolo
Krawczyk, Dorota Anna
Sadowska, Beata
Lubowicka, Bernadetta
di Schio, Eugenia Rossi
APPLIED THERMAL ENGINEERING, 2025, 261
[32] A combination of statistical methods for the analysis of the relative validation data of the quantitative food frequency questionnaire used in the THUSA study
MacIntyre, UE
Venter, CS
Vorster, HH
Steyn, HS
PUBLIC HEALTH NUTRITION, 2001, 4 (01) : 45 - 51
[33] NEW STATISTICAL-METHODS FOR DIRECTIONAL-DATA .1. BOOTSTRAP COMPARISON OF MEAN DIRECTIONS AND THE FOLD TEST IN PALEOMAGNETISM
FISHER, NI
HALL, P
GEOPHYSICAL JOURNAL INTERNATIONAL, 1990, 101 (02) : 305 - 313
[34] Adjusting data to body size: A comparison of methods as applied to quantitative trait loci analysis of musculoskeletal phenotypes
Lang, DH
Sharkey, NA
Lionikas, A
Mack, HA
Larsson, L
Vogler, GP
Vandenbergh, DJ
Blizard, DA
Stout, JT
Stitt, JP
McClearn, GE
JOURNAL OF BONE AND MINERAL RESEARCH, 2005, 20 (05) : 748 - 757
[35] Accounting for both random errors and systematic errors in uncertainty propagation analysis of computer models involving experimental measurements with Monte Carlo methods
Vasquez, VR
Whiting, WB
RISK ANALYSIS, 2005, 25 (06) : 1669 - 1681
[36] Individual participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models
Legha, Amardeep
Riley, Richard D.
Ensor, Joie
Snell, Kym I. E.
Morris, Tim P.
Burke, Danielle L.
STATISTICS IN MEDICINE, 2018, 37 (29) : 4404 - 4420
[37] A comparison of one-stage vs two-stage individual patient data meta-analysis methods: A simulation study
Kontopantelis, Evangelos
RESEARCH SYNTHESIS METHODS, 2018, 9 (03) : 417 - 430
[38] Comparison of statistical methods for the analysis of patient-reported outcomes (PROs), particularly the Short-Form 36 (SF-36), in randomised controlled trials (RCTs) using standardised effect size (SES): an empirical analysis
Qian, Yirui
Walters, Stephen J.
Jacques, Richard M.
Flight, Laura
HEALTH AND QUALITY OF LIFE OUTCOMES, 2025, 23 (01)

← 1 2 3 4 →