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Robust analysis of variance with imprecise data: an ad hoc algorithm

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dc.contributor.authorRivero Rodríguez, Carlos
dc.contributor.authorValdés Sánchez, Teófilo
dc.date.accessioned2023-06-20T03:31:03Z
dc.date.available2023-06-20T03:31:03Z
dc.date.issued2011
dc.description.abstractWe present an easy to implement algorithm, which is valid to analyse the variance of data under several robust conditions. Firstly, the observations may be precise or imprecise. Secondly, the error distributions may vary within the wide class of the strongly unimodal distributions, symmetrical or not. Thirdly, the variance of the errors is unknown. The algorithm starts by estimating the parameters of the ANOVA linear model. Then, the asymptotic covariance matrix of the effects is estimated. Finally, the algorithm uses this matrix estimate to test ANOVA hypotheses posed in terms of linear combinations of the effects.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipMEC
dc.description.sponsorshipEUROSTAT
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/20176
dc.identifier.doi10.1007/s10651-010-0155-7
dc.identifier.issn1352-8505
dc.identifier.officialurlhttp://link.springer.com/content/pdf/10.1007%2Fs10651-010-0155-7
dc.identifier.relatedurlhttp://www.springer.com
dc.identifier.urihttps://hdl.handle.net/20.500.14352/43677
dc.issue.number4
dc.journal.titleEnvironmental and ecological statistics
dc.language.isoeng
dc.page.final662
dc.page.initial635
dc.publisherSpringer
dc.relation.projectIDMTM2004-05776
dc.relation.projectID9.242.010
dc.rights.accessRightsrestricted access
dc.subject.cdu519.22
dc.subject.keywordMaximum-likelihood
dc.subject.keywordlinear-regression
dc.subject.keywordcensored-data
dc.subject.keywordem algorithm
dc.subject.keywordgrouped data
dc.subject.keywordconsistency
dc.subject.keyworderrors
dc.subject.keywordmodels
dc.subject.keywordComputational hypothesis testing
dc.subject.keywordStochastic approximation
dc.subject.keywordRobust ANOVA with precise and imprecise observations
dc.subject.keywordConditional imputation techniques
dc.subject.keywordConsistency and asymptotic distributions
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleRobust analysis of variance with imprecise data: an ad hoc algorithm
dc.typejournal article
dc.volume.number18
dcterms.referencesAnMY (1998) Logconcavity versus logconvexity: a complete characterization. J Econ Theory 80:350–369 Anido C, Rivero C, Valdes T (2000) Modal iterative estimation in linear models with unihumped errors and non-grouped and grouped data collected from different sources. Test 9(2):393–416 Anido C, Rivero C, Valdes T (2008) Analysis of variance with general errors and grouped and non-grouped data: some iterative algorithms. J Multivar Anal 99:1544–1573 Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Soc 39:1–22 Fahrmeir L,KufmannH (1985) Consistency and asymptotic normality of themaximum likelihood estimator in generalized linear models. Ann Stat 13:342–368 Healy MJR, Westmacott M (1956) Missing values in experiments analysed on automatic computers. Appl Stat 5:203–206 James IR, Smith PJ (1984) Consistency results for linear regression with censored data. Ann Stat 12:590–600 Little RJA, Rubin DB (2002) Statistical analysis with missing data. Wiley, New Jersey Louis TA (1982) Finding observed information using the EM algorithm. J Roy Stat Soc B 44:98–130 McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York Meilijson I (1989) A fast improvement of the EMalgorithm on its own terms. J Roy Stat Soc B 51:127–138 Meng XL, Rubin DB (1991) Using EM to obtain asymptotic variance-covariance matrices. J Amer Stat Assoc 86:899–909 Orchard T,WoodburyMA(1972)Amissing information principle: theory and applications. In: Proceedings of the 6th Berkeley Symposium on mathematical statistics and probability, Vol I. pp 697–715 Ritov Y (1990) Estimation in linear regression model with censored data. Ann Stat 18:303–328 TannerMA (1996) Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions. Springer, New York
dspace.entity.typePublication
relation.isAuthorOfPublication57155156-5c76-4da2-9777-5ab79884445c
relation.isAuthorOfPublication.latestForDiscovery57155156-5c76-4da2-9777-5ab79884445c

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