Communications in Statistics - Simulation and Computation 

About: Communications in Statistics - Simulation and Computation is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Estimator & Sample size determination. It has an ISSN identifier of 0361-0918. Over the lifetime, 5924 publications have been published receiving 62359 citations. The journal is also known as: Simulation and computation.

A distribution-free approach to inducing rank correlation among input variables

Abstract: A method for inducing a desired rank correlation matrix on a multivariate input random variable for use in a simulation study is introduced in this paper. This method is simple to use, is distribution free, preserves the exact form of the marginal distributions on the input variables, and may be used with any type of sampling scheme for which correlation of input variables is a meaningful concept. A Monte Carlo study provides an estimate of the bias and variability associated with the method. Input variables used in a model for study of geologic disposal of radioactive waste provide an example of the usefulness of this procedure. A textbook example shows how the output may be affected by the method presented in this paper.

Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research

Abstract: Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between treatment groups in observational studies. Standardized differences were initially developed in the context of comparing the mean of continuous variables between two groups. However, in medical research, many baseline covariates are dichotomous. In this article, we explore the utility and interpretation of the standardized difference for comparing the prevalence of dichotomous variables between two groups. We examined the relationship between the standardized difference, and the maximal difference in the prevalence of the binary variable between two groups, the relative risk relating the prevalence of the binary variable in one group compared to the prevalence in the other group, and the phi coefficient for measuring correlation between the treatment group and the binary variable. We found that a standardized difference of 10% (or 0.1) is equivalent to having a phi coefficient of 0.05 ...

How Big is a Big Odds Ratio? Interpreting the Magnitudes of Odds Ratios in Epidemiological Studies

Abstract: The odds ratio (OR) is probably the most widely used index of effect size in epidemiological studies. The difficulty of interpreting the OR has troubled many clinical researchers and epidemiologists for a long time. We propose a new method for interpreting the size of the OR by relating it to differences in a normal standard deviate. Our calculations indicate that OR = 1.68, 3.47, and 6.71 are equivalent to Cohen's d = 0.2 (small), 0.5 (medium), and 0.8 (large), respectively, when disease rate is 1% in the nonexposed group; Cohen's d 0.8 when OR > 5.

A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines

Abstract: An unbiased stochastic estimator of tr(I-A), where A is the influence matrix associated with the calculation of Laplacian smoothing splines, is described. The estimator is similar to one recently developed by Girard but satisfies a minimum variance criterion and does not require the simulation of a standard normal variable. It uses instead simulations of the discrete random variable which takes the values 1, -1 each with probability 1/2. Bounds on the variance of the estimator, similar to those established by Girard, are obtained using elementary methods. The estimator can be used to approximately minimize generalised cross validation (GCV) when using discretized iterative methods for fitting Laplacian smoothing splines to very large data sets. Simulated examples show that the estimated trace values, using either the estimator presented here or the estimator of Girard, perform almost as well as the exact values when applied to the minimization of GCV for n as small as a few hundred, where n is the number ...

Simple consistent estimators of stable distribution parameters

Abstract: The four parameters of a stable distribution may be estimated consistently from five pre-determined sample quantiles with the aid of the accompanying tables, for a in the range [0.6, 2.0] and g in the range [-1, 1]. The problem of the discontinuity of the traditional location parameter in the asymmetrical cases as a passes unity is resolved. The proposed estimators of a and c are similar to those of Fama and Roll, except that the small asymptotic bias in their estimators has been eliminated, and their restrictions that a be no less than 1.0 and that the distribution be symmetrical have been relaxed. The proposed estimators can provide good initialization values for other more efficient, but computer-intensive, methods.