Some Recent Advances in Measurement Error Models and Methods

Schneeweiß, Hans; Augustin, Thomas

doi:10.1007/3-540-32693-6_13

Cited by 7 publications

(4 citation statements)

References 54 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While several powerful procedures to correct measurement error are available for regression models (see, e.g. Wansbeek and Meijer, 2000; Cheng and Ness, 1999; Carroll et al 2006; Schneeweiß and Augustin, 2006, for surveys considering linear and nonlinear models, respectively), in the classification context well-founded treatment of measurement error is still in its infancy.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Microarray-based Classifiers: An Overview

et al. 2008

View full text Add to dashboard Cite

For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classifi cation accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifi er evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifi ers and validation strategy.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluating Microarray-based Classifiers: An Overview

et al. 2008

View full text Add to dashboard Cite

show abstract

“…We also mention other estimators briefly. For a recent review on the broader field of measurement error models, see [24]. Books on measurement error models are [2,6,9,27,33].…”

Section: Introductionmentioning

confidence: 99%

Comparing the efficiency of structural and functional methods in measurement error models

Schneeweiß¹,

Kukush²

2010

Theor. Probability and Math. Statist.

View full text Add to dashboard Cite

The paper is a survey of recent investigations by the authors and others into the relative efficiencies of structural and functional estimators of the regression parameters in a measurement error model. While structural methods, in particular the quasi-score (QS) method, take advantage of the knowledge of the regressor distribution (if available), functional methods, in particular the corrected score (CS) method, discards such knowledge and works even if such knowledge is not available. Among other results, it has been shown that QS is more efficient than CS as long as the regressor distribution is completely known. However, if nuisance parameters in the regressor distribution have to be estimated, this is no more true in general. But by modifying the QS method, the adverse effect of the nuisance parameters can be overcome. For small measurement errors, the efficiencies of QS and CS become almost indistinguishable, whether nuisance parameters are present or not. QS is (asymptotically) biased if the regressor distribution has been misspecified, while CS is always consistent and thus more robust than QS.

show abstract

“…Several authors have proposed approximations to correct estimates using heaped data [Sheppard (1897), Schneeweiss and Komlos (2009), Schneeweiss, Komlos and Ahmad (2010), Schneeweiss and Augustin (2006), Tallis (1967), Lindley (1950)]. Others have explored smoothing techniques for heaped data on the grounds that smoothing may have the effect of "spreading out" grouped responses [Hobson (1976), Singh, Suchindran and Singh (1994)].…”

mentioning

confidence: 99%

Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes

Crawford¹,

Weiss²,

Suchard³

2015

Ann. Appl. Stat.

View full text Add to dashboard Cite

Surveys often ask respondents to report non-negative counts, but respondents may misremember or round to a nearby multiple of 5 or 10. This phenomenon is called heaping, and the error inherent in heaped self-reported numbers can bias estimation. Heaped data may be collected cross-sectionally or longitudinally and there may be covariates that complicate the inferential task. Heaping is a well-known issue in many survey settings, and inference for heaped data is an important statistical problem. We propose a novel reporting distribution whose underlying parameters are readily interpretable as rates of misremembering and rounding. The process accommodates a variety of heaping grids and allows for quasi-heaping to values nearly but not equal to heaping multiples. We present a Bayesian hierarchical model for longitudinal samples with covariates to infer both the unobserved true distribution of counts and the parameters that control the heaping process. Finally, we apply our methods to longitudinal self-reported counts of sex partners in a study of high-risk behavior in HIV-positive youth.

show abstract

Some Recent Advances in Measurement Error Models and Methods

Cited by 7 publications

References 54 publications

Evaluating Microarray-based Classifiers: An Overview

Evaluating Microarray-based Classifiers: An Overview

Comparing the efficiency of structural and functional methods in measurement error models

Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes

Contact Info

Product

Resources

About