Locally minimax test of independence in elliptically symmetrical distributions with additional observations

Summary LetX=(X _ij )=(X ₁, ...,X _n )’,X’ _i =(X _i1, ...,X _ip )’,i=1,2, ...,n be a matrix having a multivariate elliptical distribution depending on a convex functionq with parameters, 0,σ. Let ϱ²=ϱ ₂ ^-2 be the squared multiple correlation coefficient between the first and the remainingp ₂+p ₃=p−1 components of eachX _i . We have considered here the problem of testingH ₀:ϱ²=0 against the alternativesH ₁:ϱ ₁ ^-2 =0, ϱ ₂ ^-2 >0 on the basis ofX andn ₁ additional observationsY ₁ (n ₁×1) on the first component,n ₂ observationsY ₂(n ₂×p ₂) on the followingp ₂ components andn ₃ additional observationsY ₃(n ₃×p ₃) on the lastp ₃ components and we have derived here the locally minimax test ofH ₀ againstH ₁ when ϱ ₂ ^-2 →0 for a givenq. This test, in general, depends on the choice ofq of the familyQ of elliptically symmetrical distributions and it is not optimality robust forQ.     

Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

ABSTRACT

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.     

Generalised cost scaling: A new approach to demographic variables in demand analysis

A new approach to modelling equivalence scales and household demand systems is introduced and its usefulness empirically demonstrated. The empirical results suggest that price effects on the general equivalence scale are more important than utility effect.