Results 51 to 60 of about 16,361 (164)

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Machine Learning: Science and Technology
Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly ...
Thomas D Swinburne, Danny Perez
doaj   +1 more source

Accuracy of attribute estimation in the crossed random effects linear logistic test model: impact of Q-matrix misspecification

Frontiers in Education
A simulation study is designed to explore the accuracy of attribute parameter estimation in the crossed random effects linear logistic test model (CRELLTM) with the impact of Q-matrix misspecification on attribute parameter estimation using the SAS ...
Yi-Hsin Chen, Isaac Y. Li, Chunhua Cao, Yan Wang   +3 more
doaj   +1 more source

Misspecification in dynamic models

, 1990
Within the class of ARMAX models we consider the effects that omitted explanatory processes have on the dynamic shape and the exogeneity properties of economic relations. We show that, under suitable assumptions, a well defined "misspecified" ARMAX model exists and is unique.
openaire   +3 more sources

On misspecifications in regularity and properties of estimators

Electronic Journal of Statistics, 2018
The problem of parameter estimation by the continuous time observations of a deterministic signal in white gaussian noise is considered. The asymptotic properties of the maximul likelihood estimator are described in the asymptotics of small noise (large siglal-to-noise ratio).
Chernoyarov, Oleg V., Kutoyants, Yury A., Trifonov, Andrei P.   +2 more
openaire   +5 more sources

Assessing misspecification of individual homogeneity assumption in multi-state models based on asymptotic theory

Journal of Biostatistics and Epidemiology, 2015
Background & Aim: Multi-state models can help better understand the process of chronic diseases such as cancers.  These models  are influenced  by assumptions  like individual  homogeneity.
Ali Zare, Mahmood Mahmoodi, Kazem Mohammad, Hojjat Zeraati, Mostafa Hosseini, Kourosh Holakouie-Naieni   +5 more
doaj  

An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout

Journal of Probability and Statistics, 2019
In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared ...
Amal Almohisen, Robin Henderson, Arwa M. Alshingiti   +2 more
doaj   +1 more source

Allele frequency misspecification: effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment

BMC Genetics, 2006
Background Studies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate.
Wilson Alexander F, Atwood Larry D, Sorant Alexa JM, Mandal Diptasri M, Bailey-Wilson Joan E   +4 more
doaj   +1 more source

Can quartet analyses combining maximum likelihood estimation and Hennigian logic overcome long branch attraction in phylogenomic sequence data? [PDF]

PLoS ONE, 2017
Systematic biases such as long branch attraction can mislead commonly relied upon model-based (i.e. maximum likelihood and Bayesian) phylogenetic methods when, as is usually the case with empirical data, there is model misspecification.
Patrick Kück, Mark Wilkinson, Christian Groß, Peter G Foster, Johann W Wägele   +4 more
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Structural Break Tests Robust to Regression Misspecification

Econometrics, 2018
Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe
Alaa Abi Morshed, Elena Andreou, Otilia Boldea   +2 more
doaj   +1 more source

Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification

Risks, 2019
The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio.
Prayut Jain, Shashi Jain
doaj   +1 more source