Bayesian Semi-Parametric and Non-Parametric Methods in Marketing and Micro-Econometrics

2012 ◽  
Author(s):  
Peter E. Rossi
Keyword(s):  
Parametric Methods ◽  
Non Parametric

Non-parametric methods reveal non-linear functional trait variation of lichens along environmental and fire age gradients

2015 ◽  
Vol 26 (5) ◽  
pp. 848-865 ◽  
Author(s):  
Peter R. Nelson ◽  
Bruce McCune ◽  
Carl Roland ◽  
Sarah Stehn
Keyword(s):  
Linear Functional ◽  
Functional Trait ◽  
Parametric Methods ◽  
Trait Variation ◽  
Non Linear ◽  
Non Parametric

Non-Parametric Methods

2017 ◽  
pp. 139-152
Author(s):  
S.D. Silvey
Keyword(s):  
Parametric Methods ◽  
Non Parametric

scholarly journals The Arctiini (Lepidoptera, Erebidae, Arctiinae) fauna of the Serra do Pardo National Park, Pará, Brazil

Check List ◽  
2015 ◽  
Vol 11 (2) ◽  
pp. 1613 ◽  
Author(s):  
José Augusto Teston ◽  
Danilo Do C. V. Correa
Keyword(s):  
National Park ◽  
New Records ◽  
Diversity Index ◽  
Sampling Site ◽  
Brazilian Amazon ◽  
The State ◽  
Parametric Methods ◽  
Light Traps ◽  
Rarefaction Curve ◽  
Non Parametric

This study evaluated the Arctiini fauna of the Serra do Pardo National Park (Pará, Brazil) between 22 September and 3 October 2011. Light traps were left one night in each sampling site (SS) from 18:00 h to 6:00 h of the next day. The following parameters were evaluated: richness (S), abundance (N), diversity index (H’), Shannon uniformity (U) index, and Berger-Parker dominance (BP). Richness was estimated using the non-parametric methods Chao1, Chao2, ACE, ICE, Jackknife1, Jackknife2 and Bootstrap. A total of 3,247 specimens were captured, belonging to 221 Arctiini taxa; 32 of these are new records for the state of Pará and, of these, six are new records for the Brazilian Amazon. The Arctiini fauna is very rich and uniform. The richness estimator and rarefaction curve indicated the need for increased sampling efforts in the area.

scholarly journals Hybrid model using logit and nonparametric methods for predicting micro-entity failure

2016 ◽  
Vol 13 (3) ◽  
pp. 35-46 ◽  
Author(s):  
A. Blanco-Oliver ◽  
A. Irimia-Dieguez ◽  
M.D. Oliver-Alfonso ◽  
M.J. Vázquez-Cueto
Keyword(s):  
Logistic Regression ◽  
Hybrid Model ◽  
Rough Set ◽  
Multilayer Perceptron ◽  
Predictive Power ◽  
Bankruptcy Prediction ◽  
Parametric Methods ◽  
Macroeconomic Variables ◽  
Accuracy Performance ◽  
Non Parametric

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Sign in / Sign up

Export Citation Format

Share Document