In this paper we consider the problem of testing for a scale change in the in®nite order moving average process X j I i 0 a i å j2i , where å j are i.i.d. r.v.s with Ejå 1 j á < I for some á > 0. In performing the test, a cusum of squares test statistic analogous to Incla Ân & Tiao's (1994) statistic is considered. It is well-known from the literature that outliers affect test procedures leading to false conclusions. In order to remedy this, a cusum of squares test based on trimmed observations is considered. It is demonstrated that this test is robust against outliers and is valid for in®nite variance processes as well. Simulation results are given for illustration.
Flexible transparent thin‐film transistors (TTFTs) have emerged as next‐generation transistors because of their applicability in transparent electronic devices. In particular, the major driving force behind solution‐processed zinc oxide film research is its prospective use in printing for electronics. Since the patterning that prevents current leakage and crosstalk noise is essential to fabricate TTFTs, the need for sophisticated patterning methods is critical. In patterning solution‐processed ZnO thin films, several points require careful consideration. In general, as these thin films have a porous structure, conventional patterning based on photolithography causes loss of film performance. In addition, as controlling the drying process is very subtle and cumbersome, it is difficult to fabricate ZnO semiconductor films with robust fidelity through selective printing or patterning. Therefore, we have developed a simple selective patterning method using a substrate pre‐patterned through bond breakage of poly(methyl methacrylate) (PMMA), as well as a new developing method using a toluene–methanol mixture as a binary solvent mixture.
We propose a new stochastic framework for analysing the dynamics of the immunity response of wildlife hosts against a disease-causing agent. Our study is motivated by the need to analyse the monitoring time-series data covering the period from 1975 to 1995 on bacteriological and serological tests-samples from great gerbils being the main host of Yersinia pestis in Kazakhstan. Based on a four-state continuous-time Markov chain, we derive a generalized nonlinear mixed-effect model for analysing the serological test data. The immune response of a host involves the production of antibodies in response to an antigen. Our analysis shows that great gerbils recovered from a plague infection are more likely to keep their antibodies to plague and survive throughout the summer-to-winter season than throughout the winter-to-summer season. Provided the seasonal mortality rates are similar (which seems to be the case based on a mortality analysis with abundance data), our finding indicates that the immune function of the sampled great gerbils is seasonal.
a b s t r a c tIn this paper, we consider the validity of the Jarque-Bera normality test whose construction is based on the residuals, for the innovations of GARCH (generalized autoregressive conditional heteroscedastic) models. It is shown that the asymptotic behavior of the original form of the JB test adopted in this paper is identical to that of the test statistic based on true errors. The simulation study also confirms the validity of the original form since it outperforms other available normality tests.
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