32 research outputs found

    On the Comparison of Run Orders of Unreplicated 2k-p-designs in the presence of a time-trend

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    The response from a factorial experiment carried out in a time sequence may be affected by uncontrollable variables that are highly correlated with the time in which they occur. In such a situation, one possibility is to randomize the run order of the experiment. Another possibility is to use a systematic run order that is robust against time-trends. Since randomized run orders make the time trend part of the error, it can be hoped that systematic run orders will be more effective to identify truly active factors. In this paper, a imulation study is used to compare the performances of the randomized and the systematic run orders. The response from an experiment where we have observed a strong time-trend is used to demonstrate the influence of a realistic time trend on the run orders under consideration. The performance of the run orders is then measured by taking the probabilities of false rejection and the probabilities of detection of active contrasts. Our results show that the randomized run order managed to keep the nominal level, while the systematic did not. Additionally, when there were active factors, then the systematic run orders did not achieve more power than did the randomized run order

    The Burden of Exile

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    Nigerian author Buchi Emecheta’s writing can be described as a form of creative expression centred on experiential knowledge. The biographical details of Emecheta’s life are present at the inception of her career as a novelist, visible in her debut novel In the Ditch (1972) and its 1974 prequel, Second-Class Citizen. These two novels, which were later published in a single volume as Adah’s Story,² explore the struggles faced by Emecheta from her childhood in Lagos to her life as a single mother of five in London. When Buchi later penned her autobiography in 1986, Head Above Water, over a decad

    CONJUGATION OF GENERALIZED GAMMA PRIOR WITH POISSON AND GENERALIZED POISSON LIKELIHOODS FOR DISEASE MAPPING

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    This article focused on the use of generalized Gamma distribution as conjugate prior with Poisson and generalized Poisson likelihoods to handle dispersion in small samples. Based on this conjugacy, Poisson-Generalized Gamma model (PGG) and Generalized Poisson-Generalized Gamma model (GPGG) are developed for Bayesian disease mapping and compared with the existing Poisson-Gamma model. The efficiency of these models was investigated using both simulated and real data applications. The deviance information criterion (DIC), dispersion test (DT), Monte Carlo error (MCE) and relative efficiency (reff) were used for comparison. All indicated that GPGG model provided the best precision and model efficiency to handle dispersion and relative risk estimation for disease mapping in small and large samples under uncontaminated and contaminated data. Thus, GPGG and PGG models served as alternative models in providing reliable mapping of diseas

    Upper Normal- CUSUM statistic for COVID-19 number of deaths.

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    Upper Normal- CUSUM statistic for COVID-19 number of deaths.</p

    Standard normal-CUSUM control chart of COVID-19 number of deaths in Nigeria.

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    Standard normal-CUSUM control chart of COVID-19 number of deaths in Nigeria.</p

    Gamma-CUSUM chart of COVID-19 number of deaths in Nigeria.

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    Gamma-CUSUM chart of COVID-19 number of deaths in Nigeria.</p
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