Statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data

In this article, the statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data is discussed. The estimation of the parameters for Gompertz distribution is obtained using maximum likelihood method (MLE) and Bayesian method under three different loss functions. We also proved the existence and uniqueness of the MLE. The one-sample Bayesian prediction intervals are obtained. The work is done for different values of the parameters. We apply the Monto Carlo simulation to compare the proposed methods, also an example is discussed to construct the Prediction intervals.     

Bayesian expectile regression with asymmetric normal distribution

In this paper, we adopt the Bayesian approach to expectile regression employing a likelihood function that is based on an asymmetric normal distribution. We demonstrate that improper uniform priors for the unknown model parameters yield a proper joint posterior. Three simulated data sets were generated to evaluate the proposed method which show that Bayesian expectile regression performs well and has different characteristics comparing with Bayesian quantile regression. We also apply this approach into two real data analysis.     

Varieties of political support in emerging democracies: A cross-national analysis

Political support is a multidimensional construct encompassing evaluations of political leaders and institutions (specific support) as well as adherence to basic regime principles (diffuse support). Scholars have traditionally assumed that diffuse and specific support are driven by different forces and evolve largely independently. Prior empirical work, however, has struggled to untangle the two support dimensions and focused predominantly on cross-national differences, ignoring their dynamics. This paper develops an analytical and empirical approach to examine the levels and dynamic interplay of both support dimensions and estimate their determinants, applying it to South American democracies between 1996 and 2015. Contrary to received wisdom, we show that both dimensions are quite volatile and closely linked in this region. In particular, negative economic shocks not only undermine support for government actors, but also fuel democratic disenchantment. Nonetheless, while regime support is rather fickle in South America, it can be ultimately resistant to performance fluctuations.     

贝叶斯方法和频率方法比较研究

频率学派和贝叶斯学派作为统计推理中的两个最重要的方法,对于这两种对立的研究方式的站队选择显然影响着研究者的研究活动。占有霸主地位的频率学派尽管是后来者,显然占有着优势的地位,而今年以来贝叶斯方法的复苏,重新向频率派发起挑战。文章从概率的产生作为开始,简单地介绍了统计的发展历程,并分别对这两种方法选择的实用、重要的可操作标准给予分析,认为两种方法各有利弊,对两种方法的选择应结合实际情况选择。     

MAP segmentation in Bayesian hidden Markov models: a case study

We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.     

Objective Bayesian analysis of counting experiments with correlated sources of background

Searches for faint signals in counting experiments are often encountered in particle physics and astrophysics, as well as in other fields. Many problems can be reduced to the case of a model with independent and Poisson-distributed signal and background. Often several background contributions are present at the same time, possibly correlated. We provide the analytic solution of the statistical inference problem of estimating the signal in the presence of multiple backgrounds, in the framework of objective Bayes statistics. The model can be written in the form of a product of a single Poisson distribution with a multinomial distribution. The first is related to the total number of events, whereas the latter describes the fraction of events coming from each individual source. Correlations among different backgrounds can be included in the inference problem by a suitable choice of the priors.     

On testing random effects in the nested-error regression model

We derive an exact F-test for random effects in the nested-error regression model. The derivation utilizes a matrix decomposition that offers a transformation of the response vector into two independent subvectors. When the random effects are absent, the test statistic reduces to a ratio of two independent residual sums of squares that are computed by fitting a regression model using each subvector. A small simulation study compares the power of the F-test with various recent tests and shows that the proposed test has a competitive performance under small as well as large number of clusters.     

Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes

We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns.