恒温槽装配和性能测试实验仪器的改进

把新型的温度测量仪器 (数字温度温差仪 )应用型物理化学实验“恒温槽装配和性能测试”中。实验证明 ,与传统的温度测量仪器 (贝克曼温度计 )相比 ,该仪器具有使用方便、精度高等优点 ,在基础实验中具有应用的实际意义     

Optimal progressive interval censoring plan under accelerated life test with limited budget

In this paper, we investigate some inference and design problems related to multiple constant-stress accelerated life test with progressive type-I interval censoring. A Weibull lifetime distribution at each stress-level combination is considered. The scale parameter of Weibull distribution is assumed to be a log-linear function of stresses. We obtain the estimates of the unknown parameters through the method of maximum likelihood, and also derive the Fisher's information matrix. The optimal number of test units, number of inspections, and length of the inspection interval are determined under D-optimality, T-optimality, and E-optimality criteria with cost constraint. An algorithm based on nonlinear mixed-integer programming is proposed to the optimal solution. The sensitivity of the optimal solution to changes in the values of the different parameters is studied.     

Age Period Cumulative Environment Model for Analyzing (Age,Period)-Tabulated Data on Breast Cancer Deaths

A model which is an alternative to the age period cohort (APC) model is proposed for analyzing (age, period)-tabulated on breast cancer deaths. The result of fitting the proposed model to the data for females in Japan is shown, where it is seen that the proposed model provides a better fit to the data than APC model in terms of AIC. Besides, the ML estimates of the parameters in the model suggest that the risks of breast cancer death existing in the environment are rapidly increasing since the period of high economic growth in Japan.     

Ridge Regression – A Simulation Study

In this article we assess the suitability of two new ridge estimators by means of a simulation study. We compare these estimators with well-known ridge estimators. We also make direct comparisons between the ordinary least squares (OLS) estimator and the ridge estimators by using ratio of the average total mean square error of the OLS estimator and the ridge estimators. We find that the new estimators perform well under certain conditions.     

Posterior Sampling When the Normalizing Constant is Unknown

This article describes a means by which to undertake Bayesian posterior inference via sampling techniques when the normalizing constant is not computable and hence unavailable. The strategy relies on the introduction of latent variables which removes any integrals associated with the inaccessibility of the normalizing constant.     

A NEW VERSION OF THE LOCAL CONSTANT M-SMOOTHER

ABSTRACT

In the case of the equally spaced fixed design nonparametric regression, the local constant M-smoother (LCM) in Chu, Glad, Godtliebsen, and Marron [1] Chu, C.K., Glad, I., Godtliebsen, F. and Marron, J.S. 1998. Edge-Preserving Smoothers for Image Processing (with discussion). Journal of the American Statistical Association, 93: 526–556. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] has the interesting property of jump-preserving. However, it suffers from the problem of boundary effects. To correct for such adverse effects on the LCM, Rue, Chu, Godtliebsen, and Marron [2] Rue, H., Chu, C.K., Godtliebsen, F. and Marron, J.S. 2001. M-smoother with Local Linear Fit. To appear in Journal of Nonparametric Statistics [Google Scholar] apply the local linear fit to the “inside” of the kernel function, and propose the local linear M-smoother (LLM). Unfortunately, the LLM is more sensitive to random fluctuations, since an extra tuning parameter is included. To avoid such a practical drawback to the LLM, we propose a new version of the LCM by applying the local linear fit to the “outside” of the kernel function. Our proposed estimator employs both the same tuning parameter associated with the ordinary LCM and the same weights assigned to the observations by the local linear smoother in Fan 3-4 Fan, J. 1992. Design-adaptive Nonparametric Regression. Journal of the American Statistical Association, 87: 998–1004. Fan, J. 1993. Local Linear Regression Smoothers and Their Minimax Efficiencies. Annals of Statistics, 21: 196–216.  . It has the same asymptotic mean square error as the LLM. In practice, it can be calculated by using the fast computation algorithm designed for the ordinary LCM by Chu et al. [1] Chu, C.K., Glad, I., Godtliebsen, F. and Marron, J.S. 1998. Edge-Preserving Smoothers for Image Processing (with discussion). Journal of the American Statistical Association, 93: 526–556. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], and does not suffer from the drawback to the LLM. More importantly, our results obtained for the new version of the LCM in the one-dimensional case can be extended directly to the multidimensional case. Simulation studies demonstrate that the asymptotic effects hold for reasonable sample sizes.     

Discrepancy in regression estimates between log-normal and gamma: some case studies

In regression models with multiplicative error, estimation is often based on either the log-normal or the gamma model. It is well known that the gamma model with constant coefficient of variation and the log-normal model with constant variance give almost the same analysis. This article focuses on the discrepancies of the regression estimates between the two models based on real examples. It identifies that even though the variance or the coefficient of variation remains constant, but regression estimates may be different between the two models. It also identifies that for the same positive data set, the variance is constant under the log-normal model but non-constant under the gamma model. For this data set, the regression estimates are completely different between the two models. In the process, it explains the causes of discrepancies between the two models.     

Robust parametric tests of constant conditional correlation in a MGARCH model

This article provides a rigorous asymptotic treatment of new and existing asymptotically valid conditional moment (CM) testing procedures of the constant conditional correlation (CCC) assumption in a multivariate GARCH model. Full and partial quasi maximum likelihood estimation (QMLE) frameworks are considered, as is the robustness of these tests to non-normality. In particular, the asymptotic validity of the LM procedure proposed by Tse (2000 Tse, Y. K. (2000). A test for constant correlations in a multivariate GARCH model. Journal of Econometrics 98 (1):107–127.[Crossref], [Web of Science ®] , [Google Scholar]) is analyzed, and new asymptotically robust versions of this test are proposed for both estimation frameworks. A Monte Carlo study suggests that a robust Tse test procedure exhibits good size and power properties, unlike the original variant which exhibits size distortion under non-normality.     

Measuring diversity in biological populations with logarithmic abundance distributions

Fisher's logarithmic distribution for species abundance is derived under a suitable set of conditions. The derivation explains the explicit meaning of the two parameters of the distribution.