Matches in SemOpenAlex for { <https://semopenalex.org/work/W872488186> ?p ?o ?g. }
Showing items 1 to 63 of
63
with 100 items per page.
- W872488186 abstract "This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk.We compare the linear logistic regression with the Gaussian process classification model in the context of bankruptcy prediction. The posterior distributions of the GPs are non-Gaussian, and we investigate the effectiveness of the Laplace approximation and the expectation propagation approximation across several different kernels for the Gaussian process. The approximate methods are compared to the gold standard of Markov Chain Monte Carlo (MCMC) sampling from the posterior.The dataset is an unbalanced panel consisting of 21846 yearly observations for about 2000 corporate firms in Sweden recorded between 1991−2008. We used 5000 observations to train the models and the rest for evaluating the predictions. We find that the choice of covariance kernel affects the GP model’s performance and we find support for the squared exponential covariance function (SEXP) as an optimal kernel.The empirical evidence suggests that a multivariate Gaussian processes classifier with squared exponential kernel can effectively improve bankruptcy risk prediction with high accuracy (90.19 percent) compared to the linear logistic model (83.25 percent)." @default.
- W872488186 created "2016-06-24" @default.
- W872488186 creator A5010915165 @default.
- W872488186 date "2015-01-01" @default.
- W872488186 modified "2023-09-26" @default.
- W872488186 title "Predicting Bankruptcy Risk: A Gaussian Process Classifciation Model" @default.
- W872488186 hasPublicationYear "2015" @default.
- W872488186 type Work @default.
- W872488186 sameAs 872488186 @default.
- W872488186 citedByCount "0" @default.
- W872488186 crossrefType "journal-article" @default.
- W872488186 hasAuthorship W872488186A5010915165 @default.
- W872488186 hasConcept C105795698 @default.
- W872488186 hasConcept C107673813 @default.
- W872488186 hasConcept C111350023 @default.
- W872488186 hasConcept C121332964 @default.
- W872488186 hasConcept C137250428 @default.
- W872488186 hasConcept C149782125 @default.
- W872488186 hasConcept C163716315 @default.
- W872488186 hasConcept C178650346 @default.
- W872488186 hasConcept C33923547 @default.
- W872488186 hasConcept C41008148 @default.
- W872488186 hasConcept C61326573 @default.
- W872488186 hasConcept C62520636 @default.
- W872488186 hasConceptScore W872488186C105795698 @default.
- W872488186 hasConceptScore W872488186C107673813 @default.
- W872488186 hasConceptScore W872488186C111350023 @default.
- W872488186 hasConceptScore W872488186C121332964 @default.
- W872488186 hasConceptScore W872488186C137250428 @default.
- W872488186 hasConceptScore W872488186C149782125 @default.
- W872488186 hasConceptScore W872488186C163716315 @default.
- W872488186 hasConceptScore W872488186C178650346 @default.
- W872488186 hasConceptScore W872488186C33923547 @default.
- W872488186 hasConceptScore W872488186C41008148 @default.
- W872488186 hasConceptScore W872488186C61326573 @default.
- W872488186 hasConceptScore W872488186C62520636 @default.
- W872488186 hasLocation W8724881861 @default.
- W872488186 hasOpenAccess W872488186 @default.
- W872488186 hasPrimaryLocation W8724881861 @default.
- W872488186 hasRelatedWork W1488066720 @default.
- W872488186 hasRelatedWork W1550862754 @default.
- W872488186 hasRelatedWork W1654787807 @default.
- W872488186 hasRelatedWork W1657213141 @default.
- W872488186 hasRelatedWork W1966025611 @default.
- W872488186 hasRelatedWork W1984465876 @default.
- W872488186 hasRelatedWork W2000259142 @default.
- W872488186 hasRelatedWork W2038048419 @default.
- W872488186 hasRelatedWork W2040676386 @default.
- W872488186 hasRelatedWork W2094779273 @default.
- W872488186 hasRelatedWork W2142575165 @default.
- W872488186 hasRelatedWork W2281212626 @default.
- W872488186 hasRelatedWork W2562737881 @default.
- W872488186 hasRelatedWork W2893982123 @default.
- W872488186 hasRelatedWork W2921801671 @default.
- W872488186 hasRelatedWork W2951249948 @default.
- W872488186 hasRelatedWork W2963737574 @default.
- W872488186 hasRelatedWork W2993337445 @default.
- W872488186 hasRelatedWork W3123551093 @default.
- W872488186 hasRelatedWork W3130974980 @default.
- W872488186 isParatext "false" @default.
- W872488186 isRetracted "false" @default.
- W872488186 magId "872488186" @default.
- W872488186 workType "article" @default.