Minimum Akaike Information Criterion Research Articles

Comparison of four analytic strategies for complex survey data: a case-study of Spanish data


 Purpose: The aim of this secondary data analysis was to investigate the effect of four different analytical strategies: Model Based Analysis (MBA), Design Based Analysis (DBA), Multilevel Model Based Analysis (MMBA), and Multilevel Design Based Analysis (MDBA), on the model estimates for complex survey data.
 Methods: Using data from the World Health Survey-Spain explanatory models for the outcome Metabolic Equivalent of Task (METs) were calculated using MBA, DBA, MMBA, and MDBA. Regression coefficients, standard errors (SE) and the Akaike Information Criterion (AIC) from all the models were compared.
 Results: DBA showed highest estimates for most of the variables, including consistently higher SE than all other model - 20% to 48% higher than estimates for MBA, 10% to 37% for MMBA and 23% to 35% for MDBA. The SE for MDBA were 2.5% to 13% higher than estimates derived from MMBA in level 1 predictors, but SE in MMBA was higher by 18% for level 2 predictors. Values of AIC suggested the model derived by MDBA was the best fit and DBA the poorest fit of the four models.
 Conclusion: With minimum AIC, MDBA appeared to be the most appropriate approach to analyze complex survey data. To confirm the finding of present study a future work on a simulation data would be required.

Predictive Autoregressive Models of the Russian Stock Market Using Macroeconomic Variables

This article evaluates the relationship of macroeconomic variables of the domestic market with the stock index on the example of the Moscow exchange and selects forecast specifications based on an integrated auto regression model - the moving average. The methods that have been used are included in integrated auto regression-moving average model with exogenous variables and seasonal component, Box&Jenkins approach, auto-arima in R function, Hyndman & Athanasopoulos approach, and maximum likelihood method. The results demonstrate that the inclusion of external regressors in the one-dimensional ARIMAX model improves its predictive characteristics. Time series of macro-indicators of the domestic market – the consumer price index, the index of the output of goods and services for basic activities are not interrelated with the index of the Moscow exchange, with the exception of the dollar exchange rate. The positive correlation between the Moscow exchange index and macro indicators of the world economy - the S&P stock index, the price of Brent oil, was confirmed. In models with minimal AIC, a rare presence of the MA component was found, which shows that the prevailing dependence of the stock market yield on previous values of the yield (AR component) and thus, better predictability of the yield. It has shown that for stock market forecasting, "manual" selection of the ARIMA model type can give better results (minimum AIC and minimum RMSE) than the built-in auto.arima algorithm in R. It is shown that from a practical point of view, when selecting forecast models, the RMSE criterion is more useful for investors, which measures the standard error of the forecast in points of the stock index.

Instability and Production Scenario of Wheat Production in Uttar Pradesh using ARIMA Model and its Role in Food Security

The main aim of this study was to assist different sectors such as producers, government, and policymakers in understanding the necessary action. Forecasting is very helpful to make decisions and minimize the risk in production. In this study, the ARIMA model was used to predict wheat's area, display, and yield in Uttar Pradesh. For this, yearly data from 1950 to 2018 were used for model fitting and forecasting up to 2025. The best fit ARIMA models were selected based on autocorrelation function and partial autocorrelation function at various lags. Forecasting performances of this model was selected based on their minimum AIC, RMSE, MAE, MAPE, and maximum R values. Model parameters were estimated by using the Gretl software and Microsoft Excel. 2 Empirical results showed that ARIMA (1,1,5) model for area, ARIMA (1,1,4) model for roduction, and ARIMA p (0,1,5) for yield model was found suitable to forecast the future of wheat in Uttar Pradesh with a 95 per cent accuracy level. Furthermore, according to the instability analysis, Uttar Pradesh had somewhat low instability in terms of area and yield but slightly high insta

Application of logistic regression, CART and random forest techniques in prediction of blast-induced slope failure during reconstruction of railway rock-cut slopes

Drilling and blasting operation is often required to excavate the infrastructure slopes for enhancing their stability or creating space for upgradation. While conducting blasting, there are many incidents of slope failure or rockfall. Thus, proper planning and careful designing of different blasting parameters are essentially required to reduce the incidents of slope failure or rockfall. In the present research, the efficacies of three machine learning (ML) techniques; Logistic Regression (LR), Classification and Regression Tree (CART) and Random Forest (RF) were examined for predicting the blast-induced slope failure (BISF) or blast-induced rockfall during reconstruction of slopes on railway route. 490 databases with thirteen variables were considered for the prediction of BISF. By applying Multicollinearity and LR technique based on minimum Akaike Information Criterion values, the six most influential input parameters were identified. With the selected input datasets, fivefold cross-validation was carried out on randomly selected five sub-groups of datasets using LR tool. Then, the best LR model having the highest prediction rate was selected and with the same training and testing datasets of the selected model, the CART and RF models were also developed. The various performance indices such as correctness, recall rate, precision, specificity, F-beta score, receiver operating characteristics (ROC) and area under the curve (AUC) were calculated to evaluate the developed models' accuracy and applicability. The developed models showed good prediction abilities, with the RF model having highest performance in terms of recall rate (90%), accuracy (96.94%) and F-beta score (0.882). The LR model has higher precision (88.9%) and AUC value (0.96) than CART and RF models. The findings of the research work demonstrate the applicability of all three models in selecting the blast design parameters to prevent BISF during blasting. The use of developed models would result in saving the commuter’s lives, avoiding traffic delays and minimising property damages in similar situations.

ANALISIS SURVIVAL DENGAN COX PROPORTIONAL HAZARD PADA KASUS DEMAM TIFOID

A common problem found in survival data is the presence of censored data. The length of hospitalization of Typhoid fever patients until declared cured is one of example of this data. Here, we use Cox regression model to analysis this data. Partial likelihood is one of the methods of estimating parameters for Cox regression model. In many cases of censored data, two objects (patients) have the same length of hospitalization (ties). Therefore, to estimate the parameters of the model must use the right method. Here we used partial likelihood Breslow, Efron, and Exact methods. The study was motivated by how the three methods performed for Cox regression model. The data used for the implementation of these methods is length of hospitalization of Typhoid fever patients at Mekar Sari Hospital-Bekasi in 2020. Based on AIC criteria, we found that exact method is the best model (minimum AIC) for parameter estimation of Cox regression model. Referring to the Cox regression model by using a significance level of 10%, there are five predictor variables that affects the length of patient hospitalization. The five variables are age, vomiting, dirty tongue, hemoglobin, and leukocyte.Keywords: Typhoid fever, Cox regression, Breslow method, Efron method, exact method.MSC2020: 62N02, 62N03

Forecast Analysis of Ghana’s Gross Domestic Product in Economic Growth using Time Series ARIMA

This paper analyses Ghana’s gross domestic product using time series Autoregressive Integrated Moving Average (ARIMA). Time series analysis involves the application of statistical models to time series data and is useful for analysing the dynamics of Gross domestic product. The Ghana’s Gross domestic products (GDP) from 1980 to 2020 were obtained from the International Monetary Fund (IMF) datasets. Box Jenkins’s methodology of time series analysis was employed to analyse the data. The autocorrelation function (ACF) and partial autocorrelation function (PACF) plot suggested an Autoregression of order one AR(1). The (ARIMA) models were obtained using the minimum AIC criteria. Model diagnostics tests were performed using Ljung-Box test. The paper established that Ghana’s GDP will incline throughout the period of 2021-2025.

Comparative evaluation of different animal models for genetic analysis of body weight traits in an organized Corriedale sheep population

The present study aimed to evaluate different animal models for their appropriateness to best fit the body weight data of Corriedale lambs at different ages and reared under temperate conditions. Data used in this study consisted of 24,907 body weight records on a total of 6162 lambs of Corriedale sheep population. The data pertained to body weight traits at different ages i.e., at birth (BW), weaning (WW), six months (SW), nine months (NW) and yearling age (YW). The (co)variance components of five traits were estimated using univariate analysis based on restricted maximum likelihood (REML) algorithm in WOMBAT program. Six different univariate animal models, with or without maternal genetic effect, maternal permanent environment effect and covariance between additive genetic and maternal genetic effects, were fitted on the body weight data of the Corriedale sheep population. All univariate animal models incorporated season (winter, spring) and period of lambing (8 periods, each of 5-year duration), and sex of lamb (male, female) as fixed effects. AIC, Log-likelihood and likelihood ratio test results were used to evaluate the best fit models for body weights at different ages. Model I, incorporating additive genetic effect of the individual as random effect, showed the minimum AIC estimate for BW. Model III showed the minimum AIC values for all other traits under consideration i.e., WW, SW, NW and YW. The direct additive genetic heritability for birth weight was 0.025 under the best fit model i.e., model I. The direct heritability estimates for WW, SW, NW and YW were 0.151, 0.216, 0.103 and 0.132, respectively under the best-fit models. Maternal genetic heritability ranged from 0.043 to 0.087 for different body weights, except BW for which model I was the best-fit. Most of the covariance and correlation estimates between direct additive genetic and maternal genetic effects were negative. Low estimates of heritability found in best-fit models in this research are suggestive of the less appropriateness of applying individual selection for this population as it may only result in slow progress for body weight traits in Corriedale sheep. Environmental factors played a significant role in generating the variation in body weight traits in Corriedale sheep.

The F- Distribution with Applications to Number of Female Patients with Cancer

The F-distribution is a particular parameterization of the beta prime distribution, which is also called the beta distribution of the second kind. This considered a new four-parameter generalized version of the Fisher Snedecor distribution called Beta- F distribution is introduced. The comprehensive account of the statistical properties of the new distributions was considered. Formal expressions for the cumulative density function, moments, and moment generating function and maximum likelihood estimation as well as its Fisher information were obtained.
 This study employed a descriptive survey research design. The target population was female patients with cancer in Lagos State University teaching Hospital for complete for 36 days. The data collected was analyzed using both descriptive statistics and presented in tables, charts and figures. Some well-known distributions were fit, some existing single distributions and the proposed F distribution to these data using the method of maximum likelihood was applied using Micro-Soft Excel 2007.
 The study established that the minimum AIC and minimum statistic on kolmogorov Smirnov of the F-distribution shows that female patients with cancer data better than the other life time distributions considered. The study concluded that Fisher-distribution is better off than existing single distribution with reference to Akaike Information Criterion (AIC) been used. It is instructive to note that the F-distribution preserves the originality of the data without transformation.

Application of Parametric Shared Frailty Models to Analyze Time-to-Death of Gastric Cancer Patients.

Despite its declining incidence, gastric cancer (GC) is one of the world's leading malignancies and a major global health concern due to its high prevalence and fatality rate. Furthermore, it is the world's fourth most common cancer and the second leading cause of cancer death. Studying the determinants of time to death of gastric cancer patients will give clinicians more information to develop specific treatment plans, forecast prognosis, and track the progress of death cases. The application of the frailty model can help account for random variation in survival that may exist due to unobserved factors, as well as show the impact of latent factors on death risk. As a result, the purpose of this study was to assess the determinants of time to death of GC patients' by applying the parametric shared frailty models. The data for this study were obtained from gastric cancer patients admitted to the Tikur Anbesa Specialized Hospital, Addis Ababa, from January 1, 2015, to February 29, 2020. With the aim of coming up with an appropriate survival model that determines factors that affect the time to death of gastric cancer patients, various parametric shared frailty models were compared. In all of the frailty models, patient regions were used as a clustering variable. The current study implemented exponential, Weibull, log-logistic, and lognormal distributions for baseline hazard functions with gamma and inverse Gaussian's frailty distributions. The performance of all models was compared using the AIC and BIC criteria. R statistical software was used to conduct the analysis. A retrospective study was undertaken on a total of 407 gastric cancer patients under follow-up at Tikur Anbesa Specialized Hospital. Of all 407 GC patients, 56.3% died while the remaining 43.7% were censored. The patients' median time to death was 21.9months, with a maximum survival time of 49.6months. In the current study, the clustering effect was significant in modeling the time to death from gastric cancer. The Weibull model with inverse Gaussian frailty has the minimum AIC and BIC value among the candidate models compared. The dependency within the clusters for the Weibull-inverse Gaussian frailty model was [Formula: see text] (13.4%). According to the results of our best model (Weibull-inverse Gaussian), the sex of the patient, the smoking status, the tumor size, the treatment taken, the vascular invasion, and the disease stage was found to be statistically significant at an alpha = 0.05 significance level. Time to death of GC patient's data set was well described by the Weibull-inverse Gaussian shared frailty. Furthermore, Weibull baseline distribution best fits the GC data set as it enables proportional hazard and accelerated failure time model, for time to failure data. There is unobserved heterogeneity between clusters (patient regions), indicating the need to account for this clustering effect. In this study, survival time to death among GC patients was discovered to be small. Covariates like older age, being male, having higher (advanced) stage of GC disease (stage three and stage four), advanced tumor size, being smoker, infected by Helicobacter pylori, and existence of vascular invasion significantly accelerate the time to death of GC patients. In contrast, talking combination of more treatments prolongs the time to death of patients. To improve the health of patients, interventions should be taken based on significant prognostic factors, with special attention dedicated to patients with such factors to prevent GC death.

Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model.

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study's selected countries.

Quantitative CT imaging features for COVID-19 evaluation: The ability to differentiate COVID-19 from non- COVID-19 (highly suspected) pneumonia patients during the epidemic period.

COVID-19 and Non-Covid-19 (NC) Pneumonia encountered high CT imaging overlaps during pandemic. The study aims to evaluate the effectiveness of image-based quantitative CT features in discriminating COVID-19 from NC Pneumonia. 145 patients with highly suspected COVID-19 were retrospectively enrolled from four centers in Sichuan Province during January 23 to March 23, 2020. 88 cases were confirmed as COVID-19, and 57 patients were NC. The dataset was randomly divided by 3:2 into training and testing sets. The quantitative CT radiomics features were extracted and screened sequentially by correlation analysis, Mann-Whitney U test, the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and backward stepwise LR with minimum AIC methods. The selected features were used to construct the LR model for differentiating COVID-19 from NC. Meanwhile, the differentiation performance of traditional quantitative CT features such as lesion volume ratio, ground glass opacity (GGO) or consolidation volume ratio were also considered and compared with Radiomics-based method. The receiver operating characteristic curve (ROC) analysis were conducted to evaluate the predicting performance. Compared with traditional CT quantitative features, radiomics features performed best with the highest Area Under Curve (AUC), sensitivity, specificity and accuracy in the training (0.994, 0.942, 1.0 and 0.965) and testing sets (0.977, 0.944, 0.870, 0.915) (Delong test, P < 0.001). Among CT volume-ratio based models using lesion or GGO component ratio, the model combining CT lesion score and component ratio performed better than others, with the AUC, sensitivity, specificity and accuracy of 0.84, 0.692, 0.853, 0.756 in the training set and 0.779, 0.667, 0.826, 0.729 in the testing set. The significant difference of the most selected wavelet transformed radiomics features between COVID-19 and NC might well reflect the CT signs. The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.

The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city

A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated Moving Average (VARIMA) model. In this research, we studied the ARIMA model which is studied with a non-constant error variance. In this case, the Autoregressive Conditional Heteroscedasticity (ARCH) model is applied to outgrow the non-constant error variance. Selection of the best model by examining the minimum AIC for each model. The ARIMA-ARCH model is implemented on rainfall data in Bandung city with Knowledge Discovery in Database (KDD) in Data Mining. The methodology in the KDD process, including pre-processing, data mining process, and post-processing. Based on the results of model fitting, the best model is the ARIMA (2,1,4)-ARCH (1) model. The result of forecasting rainfall in Bandung shows a MAPE value is 11%, which has a similar pattern with actual data for short time 2-4 days. From these results, we conclude that the ARIMA-ARCH model is a good model for forecasting the rainfall in Bandung city.

Modeling Coronavirus Pandemic Using Univariate and Multivariate Models: The Nigerian Perspective

Corona virus Disease, a disease which was discovered in December, 2019 has been spreading worldwide like wildfire. In view of this, there is need of continuous findings on the impact, consequence and possible medications of the pandemic in Nigeria and the world at large. Therefore, this research is aimed at Analyzing the spread of Coronavirus pandemic in Nigeria, using univariate and multivariate models namely;(ARIMA) and (ARIMAX). The daily data used in this research was obtained from the NCDC official website dated from 19th April, 2020 to 20th April, 2021 with total of 384 observations using R and Eview10 software for the analysis. Three different variables were examined. The variables are; total confirmed, discharged and death cases for the purpose of establishing reliable forecast, for better decision making and a helping technique for drastic action in reducing the day to day spread of the pandemic. Summary statistics and stationary test were checked with the data being stationary at the first difference and design technique was conducted as well. Also, best fitted model was selected using Akaike Information Criteria (AIC). The ARIMA (1,1,3) model with an exogenous variable was chosen from the ARIMA models with minimum AIC. From the model, a prediction of sixty-days forecast showed the upward trend of the total confirmed cases of the pandemic in the country. The government on its part via its task force can use the predicted line to take much necessary measures and emphases on taking COVID-19 vaccines so as to prevent further spread of the virus

Beta Inflated Regression Models on the Physical and Mental Health of Nigerian Stroke Survivors

Background: Stroke is one of the major public health problems worldwide. Physical and mental health data of stroke survivors are often expressed in proportions. Therefore, the Beta Regression models family for data between zero and one will be appropriate.&#x0D; Objectives: To identify a suitable model and the likely risk factors of physical and mental health of stroke survivors.&#x0D; Method: Secondary data of stroke survivors from two tertiary health Institutions in Ogun State, Nigeria, were analysed. Inflated Beta (BEINF) and Inflated-at-one-Beta (BEINF1) models were compared using Deviance (DEV), Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) for model selection. The model with minimum DEV, AIC and BIC was considered to be better.&#x0D; Results: The deviance (-86.0604,), AIC (-46.0604) and BIC (6.4391) values of the BEINF1 model for physical health and the deviance (-20.1217), AIC (19.8783) and BIC (72.3778) values of BEINF1 model for mental health were smaller than BEINF models. Therefore, BEINF1 was the better model to identify the health risk factors of stroke survivors. Age, marital status, diastolic blood pressure, disability duration and systolic blood pressure had a significant association with physical health, while BMI had a significant positive association with mental health. &#x0D; Conclusion: The beta-inflated-at-one (BEINF1) model is suitable for identifying health risk factors of stroke survivors when the outcome variable is a proportion. Both demographic and clinical characteristics were significantly associated with the health of stroke survivors. This study would assist researchers in knowing the appropriate model for analysing proportion or percentage response variables.

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

BackgroundHemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators.MethodsWe collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model.ResultsThere were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model.ConclusionsThe multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

Design and optimization of curcumin loaded nano lipid carrier system using Box-Behnken design

Herbal antioxidant like curcumin holds great potential to treat neurodegenerative disease like Alzheimer's disease. However, its therapeutic potency is obstructed due to rapid metabolism, poor solubility, GI susceptibility, enzymatic degradation and lower bioavailability. Thus, the present work aimed to design and optimize curcumin-loaded NLC (CNL) with higher drug entrapment, prolonged release and better stability. CNL was prepared by modified melt emulsification method followed by ultrasonication. The formulation was optimized by 3 factor 3 level Box-Behnken design using solid: liquid lipid, surfactant concentration and ultrasonication time as independent variable while particle size, entrapment efficiency and % drug release as dependant variable. The design suggested 3.092 solid:liquid lipid, 2.131% surfactant and 4.757 min ultrasonication fit best to get the optimized formulation. The size of the optimized CNL was noted 124.37 ± 55.81 nm, which is in the acceptable range for brain delivery. SEM results also comply with this size range (near 150 nm) and demonstrated almost spherical and uniform particles with porous and uneven surface structures. PDI, zeta potential, entrapment efficiency and % drug release were observed as 0.201 ± 0.00, − 17.2 ± 2.35 mV, 93.62 ± 0.68% and 92.73 ± 0.06%, respectively. The NLC demonstrated initial burst release with subsequent prolonged release of drug for 48 h. Weibull kinetic equation with 0.9958 R2, minimum AIC and maximum MSC value was found best fit to explain the release behavior. The β exponent and diffusional coefficient (n) indicated combined release mechanism with Fickian diffusion as drug release mechanism. Formulation was also found stable at different storage condition.

Analyzing Overdispersed Antenatal Care Count Data in Bangladesh: Mixed Poisson Regression with Individual-Level Random Effects

Poisson regression (PR) is commonly used as the base model for analyzing count data with the restrictive equidispersion property. However, overdispersed nature of count data is very common in health sciences. In such cases, PR produces misleading inferences and hence give incorrect interpretations of the results. Mixed Poisson regression with individual--level random effects (MPR_ILRE) is a further improvement for analyzing such data. We compare MPR_ILRE with PR, quasi-Poisson regression (Q_PR) and negative binomial regression (NBR) for modelling overdispersed antenatal care (ANC) count data extracted from the latest Bangladesh Demographic and Health Survey (BDHS) 2014. MPR_ILRE is found to be the best choice because of its minimum Akaike information criterion (AIC) value and the overdispersion exists in data has also been modelled very well. Study findings reveal that on average, women attended less than three ANC visits and only 6.5\% women received the World Health Organization (WHO) recommended eight or more ANC visits for the safe pregnancy and child birth. Administrative division, place of residence, birth order, exposure of media, education, wealth index and body mass index (BMI) have significant impact on adequate ANC attendance of women to reducing pregnancy complications, maternal and child deaths in Bangladesh.

Markov Regime-Switching Autoregressive Model of Stock Market Returns in Nigeria

This study is designed to model and forecast Nigeria’s stock market using the All-Share Index (ASI) as a proxy. By employing the Markov regime-switching autoregressive (MS-AR) model with data from April 2005 to September 2019, the study analyzes the stock market volatility in three distinct regimes (accumulation or distribution – regime 1; big-move – regime 2; and excess or panic phases – regime 3) of the bull and bear periods. Six MS-AR candidate models are estimated and based on the minimum AIC value, MS(3)-AR(2) is returned as the optimal model among the six candidate models. The MS(3)-AR(2) analysis provides evidence of regime-switching behaviour in the stock market. The study also shows that only extreme events can switch the ASI returns from regime 1 to regime 2 and to regime 3, or vice versa. It further specifies an average duration period of 9, 3 and 4 weeks for the accumulation/distribution, big-move and excess/panic regimes respectively which is an evidence of favorable market for investors to trade. Based on Root Mean Square Error and Mean Absolute Error, the fitted MS-AR model is adjudged the most appropriate ASI returns forecasting model. The study recommends investments in stock across the regimes that are switching between accumulation/distribution and big-move phases for promising returns.

Factors associated with coverage of vitamin a supplementation among Bangladeshi children: mixed modelling approach

BackgroundVitamin A deficiency (VAD) is a prominent and widespread public health problem in developing countries, including Bangladesh. About 2% of all deaths among under-five children are attributable to VAD. Evidence-based information is required to understand the influential factors to increase vitamin A supplementation (VAS) coverage and reduce VAD. We investigated the potential factors affecting VAS coverage and its significant predictors among Bangladeshi children aged 6 to 59 months using the VAS clustered data extracted from the latest Bangladesh Demographic and Health Survey 2014.MethodsData were analysed using mixed logistic regression (MLR) modelling approach in the generalised linear mixed model framework. The MLR model performs better than logistic regression for analysing the clustered data because of its minimum Akaike information criterion value. The likelihood ratio test showed that the variance component was significant. Therefore, the clustering effect among children was inevitable to use.ResultsVAS coverage among under-five children was 63.6%, which is not optimal and below the WHO’s recommendation and the country’s target of 90%. Children aged 25 to 36 months (AOR = 2.07, 95% CI: 1.711 to 2.513), who had higher educated mothers (AOR = 1.37, p = 0.033, 95% CI: 1.026–1.820) and fathers (AOR = 1.32, p = 0.027, 95% CI: 1.032–1.683), whose mothers had media exposure (AOR = 1.22, p = 0.006, 95% CI: 1.059–1.408) and NGO membership (AOR = 1.24, p = 0.002, 95% CI: 1.089–1.422) were more likely to consume VAS.ConclusionThe relevant authorities should create proactive awareness programs for highly vulnerable local communities, specifically targeted to educate the children’s mothers about the necessity and benefits of childhood nutrition.