Influence and residuals in restricte generalized linear models

A particular influence measure for restricted regression models is reviewed in this paper. We give em- phasis on establishing regularity conditions to apply the proposed influence measure in restricted gen- eralized linear models. The development of conditional residuals is also discussed. In particular, a sim- ulation study was conducted in order to compare the distributions of the proposed residuals for various generalized linear models. Finally, an application is given.     

A new diagnostic tool for VARMA(p,q) models

A new diagnostic method for VARMA(p,q) time series models is introduced. The procedure is based on a statistic that generalizes to a multivariate setting the properties of the usual univariate ARMA(p,q) residual correlations. A multiple version of the cumulative periodogram statistic is also suggested. Simulation studies and one real data application are presented.     

On a Characterization of Generalized Pareto Distribution Based on Generalized Order Statistics

In recent years, several attempts have been made to characterize the generalized Pareto distribution (GPD) based on the properties of order statistics and record values. In the present article, we give a characterization result on GPD based on the spacing of generalized order statistics.     

Predicting hospital census using time series regression methods

We first describe the time series modeling problem in a general way. Then some specific assumptions and observations which are pertinent to the application of these models are made. We next propose a specific approach to the modeling problem, one which yields efficient, easily calculated estimators of all parameters (under the stated assumptions). Finally, the technique is applied to the problem of modeling the census of a particular hospital.     

Bootstrap entropy test for general location-scale time series models with heteroscedasticity

This study considers a goodness-of-fit test for location-scale time series models with heteroscedasticity, including a broad class of generalized autoregressive conditional heteroscedastic-type models. In financial time series analysis, the correct identification of model innovations is crucial for further inferences in diverse applications such as risk management analysis. To implement a goodness-of-fit test, we employ the residual-based entropy test generated from the residual empirical process. Since this test often shows size distortions and is affected by parameter estimation, its bootstrap version is considered. It is shown that the bootstrap entropy test is weakly consistent, and thereby its usage is justified. A simulation study and data analysis are conducted by way of an illustration.     

The therapeutic potential of royal jelly in benign prostatic hyperplasia. Comparison with contemporary literature

The aim of this study is to establish the scientific benefit of royal jelly (RJ) on prostatic-specific antigen (PSA), post-void residual (PVR) volume and International Prostate Symptom Score (IPSS) in benign prostatic hyperplasia. For the study, a group of 40 men were administered 38?mg of RJ over a period of three months, their PSA values, prostate volumes and the volumes of their transitory prostate zones, PVR and IPPS values were measured at the end of the first month, and at the end of the third month. The results of this study confirm the potential of RJ in reducing PSA scores and improving IPSS values. Since the use of RJ did not lead to any significant reduction in PVR, prostate volume, or to any involution of the transitory zone, it appears that it may only affect the blood marker of prostatic hyperplasia and to improve quality-of-life (QoL) in those patients. Overall, in comparison to phytotherapy and conventional therapy, RJ had similar positive effects on QoL in patients with BPH, however it exhibited markedly better effects on reducing PSA levels in blood. The therapeutical use of RJ exhibited no side effects.     

PARTIALLY LINEAR MODEL SELECTION BY THE BOOTSTRAP

We propose a new approach to the selection of partially linear models based on the conditional expected prediction square loss function, which is estimated using the bootstrap. Because of the different speeds of convergence of the linear and the nonlinear parts, a key idea is to select each part separately. In the first step, we select the nonlinear components using an ' m -out-of- n ' residual bootstrap that ensures good properties for the nonparametric bootstrap estimator. The second step selects the linear components from the remaining explanatory variables, and the non-zero parameters are selected based on a two-level residual bootstrap. We show that the model selection procedure is consistent under some conditions, and our simulations suggest that it selects the true model most often than the other selection procedures considered.     

Adjusted Pearson residuals in beta regression models

In this paper, matrix formulae of order n^?1, where n is the sample size, for the first two moments of Pearson residuals are obtained in beta regression models. Adjusted Pearson residuals are also obtained, having, to this order, expected value zero and variance one. Monte Carlo simulation results are presented illustrating the behaviour of both adjusted and unadjusted residuals.     

On a recent generalization of gamma distribution

In this paper, we investigate a generalized gamma distribution recentIy developed by Agarwal and Kalla (1996). Also, we show that such generalized distribution, like the ordinary gamma distribution, has a unique mode and, unlike the ordinary gamma distribution, may have a hazard rate (mean residual life) function which is upside-down bathtub (bathtub) shaped.     

J-对称算子J-自伴扩张的谱

在J-对称算子扩张基本理论的基础上,运用Naimark谱核的方法,得到J-对称算子扩张为J-自伴算子后其谱的变化情况.