Stochastic estimation of a mixture of normal density functions using an information criterion

1970 ◽  
Vol 16 (3) ◽  
pp. 258-263 ◽  
Author(s):  
Tzay Young ◽  
G. Coraluppi
2009 ◽  
Vol 67 (3) ◽  
pp. 426-434 ◽  
Author(s):  
Simone Libralato ◽  
Cosimo Solidoro

Abstract Libralato, S., and Solidoro, C. 2010. Comparing methods for building trophic spectra of ecological data. – ICES Journal of Marine Science, 67: 426–434. The distribution of biomass, production, and catches over trophic levels (TLs) of the foodweb has been shown theoretically and empirically to provide powerful insights into ecosystem functioning and the effects of fishing. One approach for building trophic spectra of ecological data is based on smoothing original data and assuming zeroes when no values are available for a TL (smoothing-based method). An alternative method is proposed, based on the distribution of ecological data according to density functions (dispersion-based method), and a systematic review of the different alternatives is presented. Six different methods for building trophic spectra, i.e. the smoothing-based and five alternative forms for dispersion-based (using normal, lognormal, and Weibull distributions, also including shifted lognormal and Weibull with zero at TL 2), were applied to ecological properties (i.e. production, biomass, and catches) derived for 24 foodweb models to test their relative performance. The smoothing-based method suffers from the lack of consistency with original data and from unrealistic emergent properties, such as transfer efficiency. The analysis demonstrates the advantages of the dispersion-based method for overcoming these issues and shows, using transfer efficiencies estimated from the models (flow-based estimates) as a reference, that the normal density distribution function performs better.


Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 833
Author(s):  
Stephen G. Walker ◽  
Cristiano Villa

In this paper, we introduce a novel objective prior distribution levering on the connections between information, divergence and scoring rules. In particular, we do so from the starting point of convex functions representing information in density functions. This provides a natural route to proper local scoring rules using Bregman divergence. Specifically, we determine the prior which solves setting the score function to be a constant. Although in itself this provides motivation for an objective prior, the prior also minimizes a corresponding information criterion.


2016 ◽  
Vol 21 (5) ◽  
pp. 377-380
Author(s):  
Niklas Bark ◽  
Anders Kallner

1990 ◽  
Vol 29 (03) ◽  
pp. 200-204 ◽  
Author(s):  
J. A. Koziol

AbstractA basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.


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