Bayesian prediction tests for structural stability

Mills, Jeffrey A.

doi:10.1016/0304-4076(92)90018-m

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…Other well-known textbooks on econometrics do not even mention the name of robustness, like Thomas (1985), Intriligator (1978), Madansky (1976), Walters (1973), Wonnacott, R., Wonnacott, T. (1970) and Johnston (1963). More specificity is found by authors who consider robustness in forms of the error distribution (Rhodes and Fomby 1988), whereas Mills (1992) presents a Bayesian prediction test which is robust to certain forms of non-normality in the error distribution. Moreover, from the beginning Bayesian Analysis has to be characterized as cardinal, nevertheless with a high grade of arbitrariness.…”

Section: Definition Of Robustnessmentioning

confidence: 92%

Robustness of the Multi‐objective Moora Method With a Test for the Facilities Sector

Brauers

Zavadskas

2009

Technological and Economic Development of Economy

150

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The definition of robustness in econometrics, the error term in a linear equation, was not only broadened, but in addition moved to the meaning of common language: from a cardinal to a qualitative one. These interpretations were tested by an application on the Facilities Sector in Lithuania. The application is multi‐objective: like costs, experience and effectiveness at the side of the contractors; quality, duration of the work and cost price at the side of the owners. These objectives having all different units the dimensionless ratios of the MOORA method avoids the difficulties of normalization. In a first part of MOORA these ratios were aggregated and in a second one they were used as distances to a reference point. The results of both parts control each other, a test on robustness. Additionally, MOORA shows a robust domination on all other methods of multi‐objective optimization. For the Facilities Sector in Lithuania, both parts of MOORA resulted in a comparable ranking. In this way a double check was made on the robustness of the results. Santrauka Patikimumo apibrėžimas ekonometrikoje, kaip neteisingas terminas tiesinėje lygtyje, buvo ne tik papildytas, bet ir išreikštas įprasta kalba: nuo kiekybinio prie kokybinio. Šios interpretacijos buvo patikrintos taikant jas Lietuvos paslaugų sektoriuje. Taikymas yra daugiatikslis: iš rangovo pusės kaip išlaidos, patirtis, efektyvumas; kokybė, darbo trukmė, kaina iš užsakovo pusės. Minėtieji tikslai turi skirtingus matavimo vienetus. O jų santykiniai dydžiai neturi mato vienetų, todėl taikant MOORA metodą yra išvengiama sunkumų juos normalizuojant. Pirmoje MOORA metodo taikymo dalyje šie santykiai yra sujungiami, o antroje dalyje ieškoma atstumo iki geriausio sprendinio. Abiejų metodo dalių rezultatai pagrindžia sprendinio teisingumą. Tai rodo aiškų MOORA metodo pranašumą, palyginti su kitais daugiatikslio optimizavimo metodais. Taikant abi MOORA metodo dalis Lietuvos paslaugų sektoriui buvo sudarytas lyginamasis rangavimas, buvo atliktas dvigubas rezultatų patikimumo patikrinimas.

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Section: Definition Of Robustnessmentioning

confidence: 92%

Robustness of the Multi‐objective Moora Method With a Test for the Facilities Sector

Brauers

Zavadskas

2009

Technological and Economic Development of Economy

150

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“…The second advantage of the test is that the precise form of the structural change need not be specified. That property is particularly useful in highly parameterized models such as vector autoregressions (see also Mills 1992). In practice, however, the assumption of normality must be tested before the test can be applied.…”

Section: Noise ?T Is the Sequence Defined By ?T = A(l-1)ut/a(l)mentioning

confidence: 97%

Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence

Kilian¹,

Demiroğlu²

2000

Journal of Business & Economic Statistics

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“…The sample with the number is the classification result. Reference [33], [34] pointed out that NB algorithm modeling is less affected by sample size. But NB requires that all features in learning set need to meet Bayesian independent assumptions; DT and KNN do not require this [35], [36].…”

Section: ) Multiple Heterogeneous Modelsmentioning

confidence: 99%

A Cancer Diagnosis Method Combining miRNA-lncRNA Interaction Pairs and Class Weight Competition

et al. 2020

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From the perspective of data science, we propose a cancer diagnosis method combining miRNA-lncRNA interaction pairs and class weight competition. First, miRNA-lncRNA interaction data is introduced into joint expression profiles, and the complex mechanism of cancer development is demonstrated in depth through the reappearance of key association information. This is an information ensemble of three carcinogenic mechanisms at dataset construction level: classical genetics, epigenetics, and the complex interaction effect between miRNAs and lncRNAs. Then, we put forward a hybrid feature selection algorithm. By preserving the interaction relationship between miRNAs and lncRNAs, it quickly and steadily removes irrelevant and redundant features and solves the high-dimensional disaster problem of cancer expression profiles. This is an information ensemble of multiple feature selection algorithms and the significant association relationship found between multi-dimensional features at feature selection level. A diversity sampling and multi-algorithm learners are used to construct a multiple heterogeneous classification models, which overcomes the small size of normal samples and the local optimum of single algorithm and single mode. This is an information ensemble of multiple classification model structures and multiple classification model state parameters at classification modeling level. At decision level, the proposed class weight which does not depend on the sample size is constructed to address the issue of unbalanced sample class of cancers. The ensemble of multi-category multi-state information at four levels (dataset construction, feature selection, classification modeling, and decision) constitutes the framework of the proposed method. We classify BRCA, LUAD and LUSC in TCGA. Compared with the state-of-the-art classification methods, the proposed method has improved classification accuracy by 9.25%∼21.25%, sensitivity by 6.45%∼66.45%, and specificity by 10.11%. In addition, we find that lincRNA instead of miRNA always appears in each group of feature genes, which provides a new clue for the locus target selection in cancer treatment. INDEX TERMS Cancer diagnosis, joint expression profiles, miRNA-lncRNA, feature selection embedded interaction pairs, class weight competition, locus target discovery.

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Bayesian prediction tests for structural stability

Cited by 10 publications

References 13 publications

Robustness of the Multi‐objective Moora Method With a Test for the Facilities Sector

Robustness of the Multi‐objective Moora Method With a Test for the Facilities Sector

Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence

A Cancer Diagnosis Method Combining miRNA-lncRNA Interaction Pairs and Class Weight Competition

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