No sustained effect of aerobic or resistance training on insulin sensitivity in nonobese, healthy older women

It is unclear whether long-term aerobic (AT) or resistance (RT) training can improve insulin sensitivity (IS) beyond the residual effect of the last training bout in older women (54-78 years). Therefore, a group of nonobese, healthy older women underwent 6 months of AT (n = 8) or RT (n = 10), and the authors measured IS 4 days after the last training bouts using the hyperinsulinemic-euglycemic clamp technique. Women trained 3 days/week. AT consisted of 25- to 60-min sessions of walking/jogging at 60-95% of maximal heart rate. RT consisted of three sets of nine exercises repeated 10 times at 80% of 1 repetition maximum. AT decreased fat mass, whereas both AT and RT increased fat-free mass. Neither training program, however, improved absolute or relative rates of glucose disposal. The authors therefore concluded that nonobese, healthy older women should perform AT or RT on a daily basis in order to improve IS and maintain the improvement.     

On Order Statistics from Sine Distribution

We derive closed form expressions for the first two moments of order statistics from the sine distribution. For the higher moments, a recurrence relation is given. We also give a recurrence relation for the product moments. These relations will be useful for moment computations based on ordered data.     

Circular First- and Second-Order Balanced Repeated Measurements Designs

Sharma (1977 Sharma , V. K. ( 1977 ). Change-over designs with complete balance for first and second order residual effect . Canad. J. Statist. 5 : 121 – 132 .[Crossref] , [Google Scholar]) and Aggarwal et al. (2006 Aggarwal , M. L. , Deng , L.-Y. , Jha , M. K. ( 2006 ). Balanced residual treatment effects designs of first and second order . Statist. Probab. Lett. 76 : 597 – 600 .[Crossref], [Web of Science ®] , [Google Scholar]) considered non circular construction of first- and second-order balanced repeated measurements designs. Sharma et al. (2002 Sharma , V. K. , Varghese , C. , Jaggi , S. ( 2002 ). On optimality of change-over designs balanced for first and second order residual effects . Metron 60 : 153 – 162 . [Google Scholar]) constructed circular first- and second-order balanced repeated measurements designs only for a class with parameters (v, p = 3n, n = v ²) and also showed its universal optimality. In this article, we consider circular construction of first- and second-order balanced repeated measurements designs and strongly balanced repeated measurements designs by using the method of cyclic shifts. Some new circular designs with parameters (v, p, n) for cases p = v, p < v and p > v are given.     

Estimation of the parameters of a regression model with a multivariate t error variable

This paper deals with a regression model for several vari¬ables under the assumption that the errors have a multivariate t-distribution. The parameters of the model, the regression parameters, as well as the scale parameters and the degress of freedom of the error variable are estimated and the estimation procedure is illustrated by a numerical example, Also, the prop¬erties of the estimators and tests for the regression parameters are discussed.     

Distribution of sample mean when observations are correlated

It i s well known that even if the sample observations are correlated and not normal, the sample mean is normal in 1arge samples. But how large is large? This question i s investigated in this paper. In particular , the relation between the rate of convergence and the correlation property of the observations i s explored. It i s observed that the correlation, in general, retards the rate of convergence.     

Waiting time to exhaust lottery numbers

Questions related to lotteries are usually of interest to the public since people think there is a magic formula which will help them to win lottery draws. This note shows how to compute the expected waiting time to observe specific numbers in a sequence of lottery draws and show that surprising facts are expected to occur.     

Edgeworth Corrections for Realized Volatility

The quality of the asymptotic normality of realized volatility can be poor if sampling does not occur at very high frequencies. In this article we consider an alternative approximation to the finite sample distribution of realized volatility based on Edgeworth expansions. In particular, we show how confidence intervals for integrated volatility can be constructed using these Edgeworth expansions. The Monte Carlo study we conduct shows that the intervals based on the Edgeworth corrections have improved properties relatively to the conventional intervals based on the normal approximation. Contrary to the bootstrap, the Edgeworth approach is an analytical approach that is easily implemented, without requiring any resampling of one's data. A comparison between the bootstrap and the Edgeworth expansion shows that the bootstrap outperforms the Edgeworth corrected intervals. Thus, if we are willing to incur in the additional computational cost involved in computing bootstrap intervals, these are preferred over the Edgeworth intervals. Nevertheless, if we are not willing to incur in this additional cost, our results suggest that Edgeworth corrected intervals should replace the conventional intervals based on the first order normal approximation.     

A Combined Nonlinear Programming Model and Kibria Method for Choosing Ridge Parameter Regression

Ridge regression solves multicollinearity problems by introducing a biasing parameter that is called ridge parameter; it shrinks the estimates as well as their standard errors in order to reach acceptable results. Many methods are available for estimating a ridge parameter. This article has considered some of these methods and also proposed a combined nonlinear programming model and Kibria method. A simulation study has been made to evaluate the performance of the proposed estimators based on the minimum mean squared error criterion. The simulation study indicates that under certain conditions the proposed estimators outperform the least squares (LS) estimators and other popular existing estimators. Moreover, the new proposed model is applied on dataset that suffers also from the presence of heteroscedastic errors.     

Bayesian Prediction for Progressively Type-II Censored Data from the Rayleigh Model

ABSTRACT

The Rayleigh distribution is proposed to be the underlying model from which observables are to be predicted by using Bayesian approach. Progressively Type-II censored data from the Rayleigh distribution is considered and the two-sample prediction technique is used. Numerical computations and a simulation are given to illustrate the performance of the procedures.