Suchergebnisse

The study determined if the 1PL, 2PL, 3PL and 4PL item response theory models best fit the data from the 2016 NECO Mathematics objective tests. Ex-post facto design was adopted for the study. The population for the study comprised 1,022,474 candidates who enrolled and sat for June/July SSCE 2016 NECO Mathematics Examination. The sample comprised 276,338 candidates who sat for the examination in three purposively Geo political zones in Nigeria (i.e S/West, S/East and N/West). The research instruments used for the study were Optical Marks Record Sheets for the National Examination Council (NECO) June/July 2016 SSCE Mathematics objectives items. The responses of the testees were scored dichotomously. Data collected were analysed using 2loglikelihood chi-square. The results of the likelihood ratio test revealed that 2PL fitted the data better than 1PL was statistically significant (χ2 (59) = 820636.1, p 0.05); the 2PL model fitted the data better than the 1PL model; 3PL model fitted the data better than the 2PL model and The result showed that the 4PL model fitted the data better than the 3PL model and the Likelihood ratio test that 4PL model fitted the data better than 3PL model was statistically significant, (χ2 (60) = 216159.2, p 0.05). The study concluded that four-parameter logistic model fitted the 2016 NECO Mathematics test items

AbstractNonparametric model-assisted estimators have been proposed to improve estimates of finite population parameters. Flexible nonparametric models provide more reliable estimators when a parametric model is misspecified. In this article, we propose an information criterion to select appropriate auxiliary variables to use in an additive model-assisted method. We approximate the additive nonparametric components using polynomial splines and extend the Bayesian Information Criterion (BIC) for finite populations. By removing irrelevant auxiliary variables, our method reduces model complexity and decreases estimator variance. We establish that the proposed BIC is asymptotically consistent in selecting the important explanatory variables when the true model is additive without interactions, a result supported by our numerical study. Our proposed method is easier to implement and better justified theoretically than the existing method proposed in the literature.

Since the National Food Safety Initiative of 1997, risk assessment has been an important issue in food safety areas. Microbial risk assessment is a systematic process for describing and quantifying a potential to cause adverse health effects associated with exposure to microorganisms. Various dose‐response models for estimating microbial risks have been investigated. We have considered four two‐parameter models and four three‐parameter models in order to evaluate variability among the models for microbial risk assessment using infectivity and illness data from studies with human volunteers exposed to a variety of microbial pathogens. Model variability is measured in terms of estimated ED01s and ED10s, with the view that these effective dose levels correspond to the lower and upper limits of the 1% to 10% risk range generally recommended for establishing benchmark doses in risk assessment. Parameters of the statistical models are estimated using the maximum likelihood method. In this article a weighted average of effective dose estimates from eight two‐ and three‐parameter dose‐response models, with weights determined by the Kullback information criterion, is proposed to address model uncertainties in microbial risk assessment. The proposed procedures for incorporating model uncertainties and making inferences are illustrated with human infection/illness dose‐response data sets.

ABSTRACTMonetary policy has been usually analyzed in the context of small macroeconomic models where central banks are allowed to exploit a limited amount of information. Under these frameworks, researchers typically derive the optimality of aggressive monetary rules, contrasting with the observed policy conservatism and interest rate smoothing. This paper allows the central bank to exploit a wider information set, while taking into account the associated model uncertainty, by employing Bayesian model averaging with Markov chain model composition. In this enriched environment, we derive the optimality of smoother and more cautious policy rates, together with clear gains in macroeconomic efficiency.