The reliability of statistical functions in four software packages freely used in numerical computation

Almiron, Marcelo G.; Almeida, Eliana Silva de; Miranda, Marcio N.

doi:10.1214/08-bjps017

Cited by 14 publications

(18 citation statements)

References 21 publications

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“…This platform is freely available at http://www.r-project.org for a diversity of computational platforms, and its excellent numerical properties have been attested in [2,3].…”

Section: Resultsmentioning

confidence: 99%

Generalized Statistical Complexity of SAR Imagery

Almeida

Medeiros

Rosso

et al. 2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.A new generalized Statistical Complexity Measure (SCM) was proposed by Rosso et al in 2010. It is a functional that captures the notions of order/disorder and of distance to an equilibrium distribution. The former is computed by a measure of entropy, while the latter depends on the definition of a stochastic divergence. When the scene is illuminated by coherent radiation, image data is corrupted by speckle noise, as is the case of ultrasound-B, sonar, laser and Synthetic Aperture Radar (SAR) sensors. In the amplitude and intensity formats, this noise is multiplicative and non-Gaussian requiring, thus, specialized techniques for image processing and understanding. One of the most successful family of models for describing these images is the Multiplicative Model which leads, among other probability distributions, to the G 0 law. This distribution has been validated in the literature as an expressive and tractable model, deserving the "universal" denomination for its ability to describe most types of targets. In order to compute the statistical complexity of a site in an image corrupted by speckle noise, we assume that the equilibrium distribution is that of fully developed speckle, namely the Gamma law in intensity format, which appears in areas with little or no texture. We use the Shannon entropy along with the Hellinger distance to measure the statistical complexity of intensity SAR images, and we show that it is an expressive feature capable of identifying many types of targets.

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“…This platform is freely available at http://www.r-project.org for a diversity of computational platforms, and its excellent numerical properties have been attested in [2,3].…”

Section: Resultsmentioning

confidence: 99%

Generalized Statistical Complexity of SAR Imagery

Almeida

Medeiros

Rosso

et al. 2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…Although Python is now used in other scientific fields (e.g., physics), and as a teaching tool, it is much less well-known to econometricians and statisticians (exceptions are Choirat and Seri, 2009, which briefly introduces Python and contains some nice examples; and Almiron et al, 2009). We have tried to motivate Python as a powerful alternative for advanced econometric and statistical project work, but in particular as a means of linking different environments used in applied work.…”

Section: Discussionmentioning

confidence: 99%

Python for Unified Research in Econometrics and Statistics

Bilina

Lawford

2012

Econometric Reviews

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Python is a powerful high-level open source programming language that is available for multiple platforms. It supports object-oriented programming and has recently become a serious alternative to low-level compiled languages such as C++. It is easy to learn and use, and is recognized for very fast development times, which makes it suitable for rapid software prototyping as well as teaching purposes. We motivate the use of Python and its free extension modules for high performance stand-alone applications in econometrics and statistics, and as a tool for gluing different applications together. (It is in this sense that Python forms a "unified" environment for statistical research.) We give details on the core language features, which will enable a user to immediately begin work, and then provide practical examples of advanced uses of Python. Finally, we compare the run-time performance of extended Python against a number of commonly-used statistical packages and programming environments.

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“…physics), and as a teaching tool, it is much less well-known to econometricians and statisticians (exceptions are [5], which briefly introduces Python, and contains some nice examples; and [1]). We have tried to motivate Python as a powerful alternative for advanced econometric and statistical project work, but in particular as a means of linking different environments used in applied work.…”

Section: Discussionmentioning

confidence: 99%

“…>>> squares_cubes=[[x**2,x**3] for x in range(6) if x**2<=16]; print squares_cubes \ # list comprehension [[0, 0], [1,1], [4,8], [9,27], [16, 64]] >>> for x,y in enumerate(squares_cubes): print x,y, # the enumerate() function [4,8] 3 [9,27] 4 [16,64] >>> print zip(range (5),squares_cubes) # the zip(function) (2, [4, 8]), (3, [9, 27]), (4, [16, 64]…”

Section: Container Types: Lists and Dictionariesmentioning

confidence: 99%