1,721,004 research outputs found
An Objective Bayesian approach for threshold estimation in the Peaks Over the Threshold model
No full textRejoinder: Bayesian checking of the second levels of hierarchical models
Full text linkHierarchical models are increasingly used in many applications. Along with this increased use comes a desire to investigate whether the model is compatible with the observed data. Bayesian methods are well suited to eliminate the many (nuisance) parameters in these complicated models; in this paper we investigate Bayesian methods for model checking. Since we contemplate model checking as a preliminary, exploratory analysis, we concentrate on objective Bayesian methods in which careful specification of an informative prior distribution is avoided. Numerous examples are given and different proposals are investigated and critically compared. © Institute of Mathematical Statistics, 2007
Intrinsic Bayes Factors for variable selection in Weibull regression with right censored data
No full textMCMC methods to approximate conditional predictive distributions
No full textSampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given
Monte carlo comparison of four normality tests using different entropy estimates
No full textThe paper studies four entropy tests of normality of real valued observations using four statistics based on different entropy estimates from n i.i.d. observations. For the test size α = 0.05 and sample sizes 5 < n < 50, it presents critical values of the test statistics. It also compares the power and robustness of these four tests using Monte Carlo computations for sample sizes n = 10 and n -20. Preferences between the tests based on these computations can be extrapolated for small sample sizes
Recursion Removal as an Instructional Method to Enhance the Understanding of Recursion Tracing
No full textRecursion is one of the most difficult programming topics for students. In this paper, an instructional method is proposed to enhance students' understanding of recursion tracing. The proposal is based on the use of rules to translate linear recursion algorithms into equivalent, iterative ones. The paper has two main contributions: the instructional method itself, and its evaluation, which is based on previous works of other authors on mental models of recursion. As a result, an enhancement was measured in the viability of mental models exhibited by students (both for linear and multiple recursion), but no significant improvement was detected in their skills for designing recursive algorithms. Evidence was also obtained of the fact that many students with (relatively) viable mental models for linear recursion have unviable mental models for multiple recursion. Finally, it was noticed that many students adopt inaccurate mental models if those models are adequate to handle the given algorithm
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