Recent Submissions

  • Using the Seychelles child development study to cluster multiple outcomes into domains to improve estimation of the overall effect of mercury on neurodevelopment 

    LaLonde, A.; Love, T. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    Environmental exposure effects on human development can be small and difficult to detect due to the nature of observational data. In the Seychelles Child Development Study, researchers examined the effect of prenatal ...
  • Hierarchical Bayesian Bradley–Terry for applications in Major League Baseball 

    Phelan, G. C.; Whelan, J. T. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    A common problem faced in statistical inference is drawing conclusions from paired comparisons, in which two objects compete and one is declared the victor. A probabilistic approach to such a problem is the ...
  • Sizing up the regions of unique minima in the least squares nonlinear regression 

    Khinkis, L.; Crotzer M.; Oprisan, A. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    In nonlinear regression analysis, the residual sum of squares may possess multiple local minima. This complicates finding the global minimum and adversely affects reliability of the relevant statistical methods. Identifying ...
  • The relationship between anxiety and performance in a statistics class 

    Mason, S. E.; Reid, E. M. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    Many students experience anxiety when taking a required statistics course. As high levels of anxiety may interfere with performance, it is desirable to identify and control factors shown to affect student anxiety. The ...
  • Random Subspace Learning (RASSEL) with data driven weighting schemes 

    Elshrif, M.; Fokoué, E. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    We present a novel adaptation of the random subspace learning approach to regression analysis and classification of high dimension low sample size data, in which the use of the individual strength of each explanatory ...
  • Kernelized cost-sensitive listwise ranking 

    Olinto, G.; Fokoué, E. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    Learning to Rank is an area of application in machine learning, typically supervised, to build ranking models for Information Retrieval systems. The training data consists of lists of items with some partial order specified ...
  • Some equivalence relationships of regularized regressions 

    Zhang, Y.; Thakar, J.; Topham, D.; Falsey, A.; Zeng D.; Qiu, X. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)
    Regularization is a powerful framework for solving ill-posed problem and preventing model overfitting in modern regression analysis. It is especially useful for high-dimensional or functional (infinite dimensional) ...
  • The multifaceted impact of statistical methodology and theory in data science 

    Fokoué, E.; Brimkov, B. (Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2018)