Modeling the Incidence of Maize Spotted Stem-Borer (Chilo partellus) Infestation Under Long-Term Organic and Conventional Farming Systems
Wainaina Stephen,
Anthony Waititu,
Daisy Salifu,
Samuel Mwalili,
Edward Karanja,
Noah Adamtey,
Henri Tonnang,
Felix Matheri,
Edwin Mwangi,
David Bautze,
Chrysantus Tanga
Issue:
Volume 8, Issue 6, December 2022
Pages:
169-181
Received:
7 October 2022
Accepted:
24 October 2022
Published:
4 November 2022
DOI:
10.11648/j.ijdsa.20220806.11
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Abstract: The damage levels of the maize spotted stem borers (Chilo partellus Swinhoe) are estimated at 400,000 metric tons, which is equivalent to 13.5% of farmers' annual maize harvest accounting for US$80 million. Despite the economic importance of the pest, information on the incidence under long-term organic and conventional farming systems is lacking. This study evaluated three different link functions [logit, probit, and complementary log-log – (clog-log)] to reduce prediction errors in overdispersed stem borer incidence data for 12 years in four farming systems. The clog-log link function had the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) indexes for the pest incidence model in Thika. Contrarily, probit showed the lowest AIC and BIC in the Chuka incidence data model. The residual diagnostic plots with clog-log demonstrated no patterns against the predicted values. Our findings revealed that clog-log link function provided the best fit in beta-binomial mixed models compared to others. We advocate for the use of clog-log for long-term pest incidence data modelling to obtain biologically realistic projections. Users of mixed models must incorporate explicit consideration of suitable link function discrimination, model fit and model complexity into their decision-making processes if they build biologically realistic models.
Abstract: The damage levels of the maize spotted stem borers (Chilo partellus Swinhoe) are estimated at 400,000 metric tons, which is equivalent to 13.5% of farmers' annual maize harvest accounting for US$80 million. Despite the economic importance of the pest, information on the incidence under long-term organic and conventional farming systems is lacking. ...
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Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria
Nwakuya Maureen Tobechukwu,
Biu Oyinebifun Emmanuel,
Benson Tina Ibienebaka
Issue:
Volume 8, Issue 6, December 2022
Pages:
182-186
Received:
1 November 2022
Accepted:
15 November 2022
Published:
23 November 2022
DOI:
10.11648/j.ijdsa.20220806.12
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Abstract: This article proposed the modelling of the daily COVID-19 confirmed cases in Nigeria using a Self-Exciting Threshold Autoregressive (SETAR) model. Coronavirus also known as Covid-19 first appeared in Wuhan in December 2019 and quickly spread across the world and became a major phenomenon confronting humanity today. Since the outbreak of COVID-19, several models have been introduced to study the virus and recommend appropriate policy direction to tackle the pandemic. Due to the nonlinear behavior of the series, the Self-Exciting Threshold Autoregressive (SETAR) model was adopted. The series was found to be nonstationary series which was differenced twice to achieve stationarity. The series exhibited nonlinearity with evidence of a structural break. A SETAR (2, 4, 1) model was identified as the most fitted model to the data. Furthermore, the identified SETAR nonlinear model was used to obtain a one month period forecasts for the daily confirmed COVID-19 cases. The forecast accuracy measure were used to verify that SETAR (2, 4, 1) was the best fitted model and forecast for the month of January 2023 was presented. The result also evidenced that the number of daily confirmed cases is expected to increase from 281,526 cases in year 2022 to 312,776 cases in year 2023.
Abstract: This article proposed the modelling of the daily COVID-19 confirmed cases in Nigeria using a Self-Exciting Threshold Autoregressive (SETAR) model. Coronavirus also known as Covid-19 first appeared in Wuhan in December 2019 and quickly spread across the world and became a major phenomenon confronting humanity today. Since the outbreak of COVID-19, s...
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Logistic Estimation Method in the Presence of Collinearity and It’s Application
Runyi Emmanuel Francis,
Maureen Tobe Nwakuya
Issue:
Volume 8, Issue 6, December 2022
Pages:
187-193
Received:
18 November 2022
Accepted:
6 December 2022
Published:
15 December 2022
DOI:
10.11648/j.ijdsa.20220806.13
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Abstract: Regression analysis is a widely used statistical technique in investigating relationships between the response variable and outcome variable. The logistic regression examines the relationship between variables when the response variable has a dichotomous output i.e., has two possible levels and outcome variable which could be categorical or continuous. Logistic regression using maximum likelihood estimation has gained wide use in determining the parameter estimate but, in the case, where the covariates are correlated, there is an inflation in the variance, standard error of the estimator and high coefficient of determination for the regression model, leading to the problem of multicollinearity in the regression model, thereby resulting to an incorrect conclusion about the relationship among these variables, hence the traditional method of estimating the parameters fails and becomes unstable. To attempt addressing the presence of multicollinearity in the regression model, various methods have been proposed which includes Ridge estimator, Stein estimator, Bayesian estimator and Liu estimators. We therefore propose a modified estimator for estimating the parameter of the logit model in the presence of multicollinearity by modifying the existing Liu logistic estimator. The modified estimator is applied to real life data. Results showed that the Modified Liu Logistic estimator outperformed the existing estimators considered in this study, in terms of smaller variance, bias and the MSE of the estimator.
Abstract: Regression analysis is a widely used statistical technique in investigating relationships between the response variable and outcome variable. The logistic regression examines the relationship between variables when the response variable has a dichotomous output i.e., has two possible levels and outcome variable which could be categorical or continu...
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