Simulating dependent binary variables through multinomial sampling

The study aimed to use Logistic Regression to categorize the binary variables which don't follow the natural distribution national corporation data were used at the gifted and distinguished schools affiliated to the Ministry of Education in Sudan, The study used the descriptive, deductive and analytic methods, that through analyzing the study data, besides using the Statistic Package for Social Sciences (SPSS version 20) to treat data. The study concluded to where the authentic categorize rate of the logistic declension about (92%), and that the most important that effects the acceptance is the Wechsler test for measuring the intelligence talented, outstanding has no significant impact on the model, out of eleven variables and the rest of the variables have no significant on the model.

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Some Illustrations of Information Geometry in Biology and Physics

Handbook of Research on Computational Science and Engineering - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-61350-116-0.ch013 ◽

2012 ◽

pp. 287-315 ◽

Cited By ~ 3

Author(s):

C. T. J. Dodson

Keyword(s):

Amino Acid ◽

State Space ◽

Information Geometry ◽

Probability Density Functions ◽

Density Functions ◽

Binary Variables ◽

Information Theoretic ◽

Precise Meaning ◽

Rate Processes ◽

Intuitive Sense

Many real processes have stochastic features which seem to be representable in some intuitive sense as `close to Poisson’, `nearly random’, `nearly uniform’ or with binary variables `nearly independent’. Each of those particular reference states, defined by an equation, is unstable in the formal sense, but it is passed through or hovered about by the observed process. Information geometry gives precise meaning for nearness and neighbourhood in a state space of processes, naturally quantifying proximity of a process to a particular state via an information theoretic metric structure on smoothly parametrized families of probability density functions. We illustrate some aspects of the methodology through case studies: inhomogeneous statistical evolutionary rate processes for epidemics, amino acid spacings along protein chains, constrained disordering of crystals, distinguishing nearby signal distributions and testing pseudorandom number generators.

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Measuring Recurrent Event Features in Univariate Data

The Econometric Analysis of Recurrent Events in Macroeconomics and Finance ◽

10.23943/princeton/9780691167084.003.0005 ◽

2016 ◽

Author(s):

Don Harding ◽

Adrian Pagan

Keyword(s):

Recurrent Events ◽

Recurrent Event ◽

Binary Variables ◽

Bear Markets ◽

Bull Markets

This chapter begins with a discussion of why we would expect to find that the time spent in expansions (bull markets, etc.) would be much greater than the time spent in contractions (bear markets, etc.). By focusing on the probabilities of getting particular outcomes for the binary variables summarizing the recurrent events, we can provide an explanation of this long-observed feature. The remainder of the chapter looks at many proposals for summarizing other features of the recurrent events. These involve well-known quantities such as durations and amplitudes, as well as lesser known ones, such as the sharpness of peaks and troughs.

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