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Estimating the Context Effect in a Multilevel Latent Model with Small Sample Sizes: A Monte Carlo Simulation Study
Issue:
Volume 6, Issue 5, September 2017
Pages:
221-227
Received:
3 August 2017
Accepted:
11 August 2017
Published:
4 September 2017
Abstract: In multilevel modeling, the relationships between the criterion and predictors are investigated at different levels. Often, the cluster-level predictors are measured by aggregating the individual-level measures. However, the aggregated cluster-level predictors do not always reliably measure the cluster-level regression coefficient, and therefore the context coefficient. This study investigates an alternative approach: estimating cluster-level predictor on the latent cluster mean by using multilevel latent. A comparison is made of the accuracy of the context coefficient and standard error under a wide range of conditions. Results reveal that bias for context effect is small in multilevel latent model. Maximum likelihood (ML) estimator yields more accurate standard error estimation than robust maximum likelihood (MLR) when cluster number is small (less than 50). Very small cluster sample sizes (less than 10) should be avoided because they lack power and empirical sampling variance.
Abstract: In multilevel modeling, the relationships between the criterion and predictors are investigated at different levels. Often, the cluster-level predictors are measured by aggregating the individual-level measures. However, the aggregated cluster-level predictors do not always reliably measure the cluster-level regression coefficient, and therefore th...
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Performance Rating of the Exponentiated Generalized Gompertz Makeham Distribution: An Analytical Approach
Ogunde Adebisi Ade,
Fatoki Olayode,
Ajayi Bamidele
Issue:
Volume 6, Issue 5, September 2017
Pages:
228-235
Received:
22 April 2017
Accepted:
2 May 2017
Published:
4 September 2017
Abstract: We developed a five parameter distribution known as the Generalized Exponentiated Gompertz Makeham distribution which is quite flexible and can have a decreasing, increasing and bathtub-shaped failure rate function depending on its parameters making it more effective in modeling survival data and reliability problems. Some comprehensive properties of the new distribution, such as closed-form expressions for the density function, cumulative distribution function, hazard rate function, moment generating function and order Statistics were provided as well as maximum likelihood estimation of the Generalized Exponentiated Gompertz Makeham distribution parameters and at the end, in order to show the distribution flexibility, an application using a real data set was presented.
Abstract: We developed a five parameter distribution known as the Generalized Exponentiated Gompertz Makeham distribution which is quite flexible and can have a decreasing, increasing and bathtub-shaped failure rate function depending on its parameters making it more effective in modeling survival data and reliability problems. Some comprehensive properties ...
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Using the Second Order Kronecker Model in Simplex –Centroid Design in Formulating Optimum Dairy Feed
Robert Muriungi Gitunga,
Joseph Kipsigei Koske,
Johnstonne Mutiso Muindi
Issue:
Volume 6, Issue 5, September 2017
Pages:
236-247
Received:
18 March 2017
Accepted:
8 April 2017
Published:
8 September 2017
Abstract: The Second order kronecker model for simplex-centroid design was fitted where data was for feed supplements blend using a mixture of soya beans, maize jam, cotton seed and fish meal guided by design points in the simplex-centroid design and the respose was the yield of milk in litres. The main objective was to fit a Kronecker model in the simplex-centroid design to formulate optimum dairy meal concentrates. Use the data to fit the second order kronecker model for four components simplex-centroid design. From the Kronecker regression function, coefficient matrix was derived from selected parameter subsystem of interest, moment matrix was then obtained. Information matrix and improved information matrix were derived. The collected data was fitted in the derived Kronecker model and the estimates of the parameters as well as overall model performance were numerically obtained. ANOVA was run to incorporate the constant term. From the analysis it was found that Kronecker model provided a good fit. Therefore the results support that the feed supplement had significant effect to milk productivity. For optimal production the research recommend that more than one ingredients need to be blend. Blends with soya beans and fish meal in two, three and four ingredients were statistically significant and therefore recommended for optimal milk production. From the ANOVA it was found that other factors not included in this study affect milk productivity and therefore the research recommends further studies be done to investigate those other factors such as the breeds, feeding practices and also effect of supplement to other dairy products.
Abstract: The Second order kronecker model for simplex-centroid design was fitted where data was for feed supplements blend using a mixture of soya beans, maize jam, cotton seed and fish meal guided by design points in the simplex-centroid design and the respose was the yield of milk in litres. The main objective was to fit a Kronecker model in the simplex-c...
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Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure
Issue:
Volume 6, Issue 5, September 2017
Pages:
248-253
Received:
18 May 2017
Accepted:
12 June 2017
Published:
18 September 2017
Abstract: This paper aimed at assessing the performance of some estimators in the presence of one-sided exponential heteroscedasticity structure in panel model estimation. This study employs Monte Carlo experiments to evaluate the performances. It focuses on random effects models with 150 and 300 as cross-sectional units (N) and 10 and 20 as time periods (T) with Absolute Bias (ABIAS) and Root Mean Squared Error (RMSE) were criterion for assessing the performances of the estimators. The estimators were then ordered according to their performances. Generally, the performance improved as the combinations of N and T increased in experiments. The ranking of the eight estimators for the experiment are in the order: PGLS (95%), SWAR (69%), NER (64%), WG (45%), AM (43%), WALHUS (37%), BG (36%) and POLS (28%). Panel generalised least squares estimator (PGLS) outperformed other estimators in the presence of OEHS, using POLS as a known benchmark to gauge the performance and the work will help in the choice of estimators when faced with empirical datasets that exhibit exponential heteroscedasticity.
Abstract: This paper aimed at assessing the performance of some estimators in the presence of one-sided exponential heteroscedasticity structure in panel model estimation. This study employs Monte Carlo experiments to evaluate the performances. It focuses on random effects models with 150 and 300 as cross-sectional units (N) and 10 and 20 as time periods (T)...
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