A linear negative multinomial model

A linear model is defined for logarithmic and linear functions of negative multinomial frequency counts. A closed-form estimator of the model parameters, estimator covariance matrix, and general Wald test are derived under the assumption of negative multinomial sampling.     

Empirical Bayes approach to multiparameter estimation: with special reference to multinomial distribution

Empirical Bayes approach to estimation of many parameters is considered. Special features of the techniques discussed are: (i) the handling of unequal sample sizes at various stages of an Empirical Bayes sampling scheme and (ii) a general iterative procedure for estimating the parameters of a parametric prior distribution based on the likelihood approach. Linear empirical Bayes estimation is also considered. Application of the general techniques is demonstrated with special reference to a multinomial data distribution.     

Testing for the maximum cell probabilities in multinomial distributions

This paper investigates one-sided hypotheses testing for p, the largest cell probability of multinomial distribution. A small sample test of Ethier (1982) is extended to the general cases. Based on an estimator of p, a kind of large sample tests is proposed. The asymptotic power of the above tests under local alternatives is derived. An example is presented at the end of this paper.     

Multiparameter estimation for some multivariate discrete distributions with possibly dependent components

Summary In multiparameter estimation for multivariate discrete distributions with infinite support, inadmissibility problems in situations where the multivariate probability distribution function isnot a product of the one-dimensional marginal probability distribution functions have previously been unexplored. This paper examines the inadmissibility problem in some of these situations. Special attention is given to estimating the mean of a negative multinomial distribution. In estimating the mean vector, certain Clevenson-Zidek type estimators are shown to be uniformly better than the usual estimator under a large class of generally scaled squared loss functions. Some of the results are generalized to other multivariate discrete distributions and to situations where several independent negative multinomial distributions are considered.     

A bayesian approach to nonparametric test problems

We propose an alternative approach to the classical nonparametric test problems, such as the goodness of fit test and the two-sample nonparametric test. In this approach, those problems are reviewed from the viewpoint of the estimation of the underlying population distributions and are formulated as the problem of model selection between Bayesian models which were recently proposed by the present authors. The model selection can be easily realized by choosing a model with the smallest ABIC, Akaike Bayesian information criterion. The approach provides the estimates of the density of the underlying population distribution(s) of any shape as well as the evaluation of the goodness of fit or the check of homogeneity of distributions. The practical utility of the present procedure is demonstrated by numerical examples. The difference in behavior between the present procedure and a density estimator GALTHY proposed by Akaike and Arahata is also briefly discussed.This paper was originally read at the Conference on Graphical Models to Analyze Structures (Organizer: N. Wermuth, Johannes Gutenberg University), June 30-July 2, 1986, Wiesbaden, West Germany.     

浅谈大数据时代政府网络舆情管理策略

21世纪是信息技术飞速发展的时代,人们的生产生活中越发依赖信息技术,信息技术提高了生产效率、提升了生活质量,为人们带来了诸多便捷。但信息技术的运用也存在许多问题,人们对于互联网的应用越发广泛,同时也会接触多元化的信息,易受网络环境的影响,此时应发挥政府网络舆情管理的作用,减少网络舆情所带来的负面影响,保证社会发展的稳定性。文章主要针对大数据时代政府网络舆情管理的相关内容进行分析,作出了如下阐述。     

如何提高地方电视新闻的竞争力

地方电视台在新闻资讯的获取方面,无论是在速度还是数量上都无法和央视以及各大卫视相比,因此,在电视新闻的播报和影响力方面,地方电视台一直处于劣势。尤其是近年来网络媒体的兴起,使我们的地方电视新闻发展举步维艰,如何提高地方电视新闻的竞争力,成为摆在每个电视新闻人面前急需解决的问题。从实际出发,多角度探讨地方电视台如何利用自身优势、发挥本地特色来提升竞争力。     

Q2ABR: QoE‐aware adaptive video bit rate solution

In this paper, we propose a new adaptive bit rate (ABR) streaming method. This method is based on estimating and monitoring users' video streaming experience, their quality of experience (QoE). This ensures a good user QoE and optimises bandwidth utilisation by monitoring video buffer fill rate to ensure minimal data traffic. First, we achieve a QoE evaluation model based on network bandwidth, video segment representation, and dropped video frame rate parameters. Second, following our QoE evaluation model, we formulate an ABR method using the reinforcement learning (RL) paradigm to select video representations and using a breakpoint detection mechanism to monitor end‐user QoE variation. The proposed ABR method is called “QoE‐aware adaptive bit rate (Q2ABR)” and is composed of three individual modules, one for QoE estimation using machine learning methods, one for QoE variation monitoring using the breakpoint detection mechanism, and one for video representation selection using reinforcement learning. The design objective of Q2ABR is to ensure the overall QoE of these users while maintaining a minimum variation in the standard deviation of the users' QoE values. Third, the performance of the Q2ABR method is evaluated and compared with several existing ABR approaches in the literature using real traces that we collect on different transport scenarios (such as bus and train, among others). Since this method considers the user's perception of video quality as a regulator for optimising the overall video distribution network, good results are ensured in terms of the user's experience and buffer fill rate.     

Analytic‐based product opinion detection algorithm for twitter microblogging network

Twitter, with an ever‐increasing user base, has greatly influenced the opinion and purchase habits of the common masses. This has in turn forced the product firms to get involved with sentiment analysis which enables them to mine the actual opinion about their product and make business decisions accordingly. Even though a majority of the existing methods detect sentiment of the tweet with a reasonable accuracy, few ignore emoticons while others consider them as stop words. Emoticons have enabled the users to express their emotion more accurately which eliminates the ambiguity that can arise with usage of words. The trending popularity of emoticons among the users combined with its ease of usage makes it highly lucrative in sentiment analysis. Hence, mining the product opinion without considering the emoticons will severely undermine the accuracy and reliability of the opinion. Moreover, sarcasm detection is still an uncharted territory in opinion mining and is exceedingly difficult to factor it in. Sarcastic tweets when left undetected will affect the accuracy of the opinion. Therefore, the polarity of the individual words and emoticons of the tweets are computed using linguistic analysis. The sarcastic tweets are then classified and eliminated based on their anomalous polarity. By placing a higher emphasis on emoticons, the proposed emoticon‐based linguistic opinion algorithm yields satisfactory results when compared with other traditional and state of the art approaches.