A Sequential Shrinkage Estimating Method for Tobit Regression Model

In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.

New Facts in Regression Estimation under Conditions of Multicollinearity

This paper considers the approaches and methods for reducing the influence of multi-collinearity. Great attention is paid to the question of using shrinkage estimators for this purpose. Two classes of regression models are investigated, the first of which corresponds to systems with a negative feedback, while the second class presents systems without the feedback. In the first case the use of shrinkage estimators, especially the Principal Component estimator, is inappropriate but is possible in the second case with the right choice of the regularization parameter or of the number of principal components included in the regression model. This fact is substantiated by the study of the distribution of the random variable , where b is the LS estimate and β is the true coefficient, since the form of this distribution is the basic characteristic of the specified classes. For this study, a regression approximation of the distribution of the event based on the Edgeworth series was developed. Also, alternative approaches are examined to resolve the multicollinearity issue, including an application of the known Inequality Constrained Least Squares method and the Dual estimator method proposed by the author. It is shown that with a priori information the Euclidean distance between the estimates and the true coefficients can be significantly reduced.

Sensitivity Test and Data Analysis for Storage Reliability Assessment of Explosive Initiator

The explosive initiator is one kind of sensitivity products with long life and high reliability.In order to improve the storage reliability assessment,the method of storage reliability assessment for explosive initiator was proposed based on time series model using the sensitivity test data.In the method,the up and down test was used to estimate the distribution parameters of threshold.And an approach to design the up and down test was present to draw better estimations.Furthermore,the method of shrinkage estimation was introduced to get a better estimation of scale parameter by combining the sample information with prior information.The simulation result shows that the shrinkage estimation is better than traditional estimation under certain conditions.With the distribution parameters estimations,the time series models were used to describe the changing trends of distribution parameters along with storage time.Then for a fixed storage time,the distribution parameters were predicted based on the models.Finally,the confidence interval of storage reliability was obtained by fiducial inference.The illustrative example shows that the method is available for storage reliability assessment of the explosive initiator with high reliability.

IMPROVED ESTIMATES OF THE COVARIANCE MATRIX IN GENERAL LINEAR MIXED MODELS

In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic mean, respectively, are proposed. It is shown that these new estimators dominate the unbiased estimator under the squared error loss function. Finally, some simulation results to compare the performance of the proposed estimators with that of the unbiased estimator are reported. The simulation results indicate that these new shrinkage estimators provide a substantial improvement in risk under most situations.

Optimal Estimation for Power of Variance with Application to Gene-Set Testing

Detecting differential expression of genes in genom research(e.g.,2019-nCoV)is not uncommon,due to the cost only small sample is employed to estimate a large number of variances(or their inverse)of variables simultaneously.However,the commonly used approaches perform unreliable.Borrowing information across different variables or priori information of variables,shrinkage estimation approaches are proposed and some optimal shrinkage estimators are obtained in the sense of asymptotic.In this paper,we focus on the setting of small sample and a likelihood-unbiased estimator for power of variances is given under the assumption that the variances are chi-squared distribution.Simulation reports show that the likelihood-unbiased estimators for variances and their inverse perform very well.In addition,application comparison and real data analysis indicate that the proposed estimator also works well.

Estimation in the Complementary Exponential Geometric Distribution Based on Progressive Type-II Censored Data

Complementary exponential geometric distribution has many applications in survival and reliability analysis.Due to its importance,in this study,we are aiming to estimate the parameters of this model based on progressive type-II censored observations.To do this,we applied the stochastic expectation maximization method and Newton-Raphson techniques for obtaining the maximum likelihood estimates.We also considered the estimation based on Bayesian method using several approximate:MCMC samples,Lindely approximation and Metropolis-Hasting algorithm.In addition,we considered the shrinkage estimators based on Bayesian and maximum likelihood estimators.Then,the HPD intervals for the parameters are constructed based on the posterior samples from the Metropolis-Hasting algorithm.In the sequel,we obtained the performance of different estimators in terms of biases,estimated risks and Pitman closeness via Monte Carlo simulation study.This paper will be ended up with a real data set example for illustration of our purpose.