A PRISMA-IPD systematic review and meta-analysis: does age and follow-up improve active range of motion of the wrist and forearm following pediatric upper extremity cerebral palsy surgery?
Given a set of observations, the knowledge of the underlying probability density function that ge... more Given a set of observations, the knowledge of the underlying probability density function that generates the sample is often of interest. Kernel Density Estimation is a nonparametric method used to guess the underlying density function using the sample observations. Although arguably the most popular method of density estimation, KDE is not free from drawbacks. This method of estimation varies greatly with the choice of the smoothing parameter used to estimate the density. This paper gives an overview of the KDE and discusses some statistical properties of the ideal estimator used to guess the unknown density. An outline of some existing methods of choosing a smoothing parameter are discussed. Here we only consider estimation under the univariate setup. The idea of KDE can easily be generalized to a multivariate dataset.
In any testing problem, the most popular procedure to draw a conclusion regarding the null and al... more In any testing problem, the most popular procedure to draw a conclusion regarding the null and alternative hypotheses is to use the p-value of the test. If the p-value is below a certain level of significance “the test is rejected” and if not “we fail to reject the null hypothesis based on the observed data”. What if we wanted to know how much more favoured the alternative hypothesis was, based on the data observed, than the null hypothesis. The p-value only taking into account the distribution under the null setup fails to answer this question. This is primarily why we use the Bayes Factor. The purpose of this paper is to provide a brief overview of the Bayes Factor. Using two simple examples the use of Bayes Factor in testing problems is depicted and conclusions drawn. The paper tries to establish the Bayes Factor as another practical tool for testing of hypotheses.
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Papers by Onrina Chandra