Model-building of multiple binary logit using model averaging

Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC

LASSO Regression in Consumer Price Index Malaysia

This study is aimed to determine the factors contributing to the prediction of the total Consumer Price Index (CPI) in Malaysia through model selection using LASSO regression. The outliers are identified using the leverage values and studentized deleted residuals while the multicollinearity variables will undergo progressive elimination based on Variance Inflation Factor (VIF) values. K-fold Cross-Validation (CV) method and Mean Square Error of Prediction (MSE(P)) were used to identify the best model. Model-building without removal of outliers (Set A), model-building with the remove outliers based on leverage points and studentized deleted residuals (Set B), model-building after removal of extreme outliers based on the boxplot (Set C) were carried out. The multicollinearity variables were removed for all the three sets. The results showed that the MSE(P) of the best LASSO model in Set C is the smallest compared to the other two sets. The nine major categories such as food and non-alcoholic beverages, alcoholic beverages and tobacco, clothing and footwear, transport, communication, recreation service and culture, education, restaurants and hotels, miscellaneous goods and services have significant contribution in prediction of the total CPI in Malaysia

Model-building on survivability of upper gastrointestinal bleed patient’s

Statistical modelling by using Multiple Binary Logit (MBL) or Logistic Regression (LR) on a medical data has been a common practice by the researchers. However, there are no agreed guidelines for how best to carry out model-building using MBL to obtain the best model. This research will propose an appropriate guideline for beginners and demonstrate the flow of model-building process using Rockall score data as well as highlighting the significant factors of survivability of Upper Gastrointestinal bleed (UGIB) patients in Sabah. Rockall score is a scoring system used in identifying the risk of survivability for patients with UGIB. The patient’s data were retrieved from Hospital Queen Elizabeth in Sabah. Seven categorical variables related to the Rockall scoring system were studied and the steps to obtain best model using MBL were illustrated in four phases. The phases include all possible models, selected models, best model and goodness-of-fit test. All possible models were considered without interaction variables. A progressive elimination (one by one, least significant first) of the insignificant variables is carried out to a set of selected models (with significant variables). Model selection criteria AIC, corrected AIC (AICc) and BIC were used to single out the best model among the selected models. Pearson chi-square test and deviance chi-square test were carried out to ensure the best model validity and appropriateness. The results showed that the factors affecting the survivability of UGIB patients in Sabah are shock score, comorbidity and rebleed. In conclusion, the study showed that the Rockall scoring system had satisfactory validity for the prediction of shock score, comorbidity and rebleeding in patients with UGIB. There was a negative relationship between the clinical Rockall scores and patient outcomes in terms of shock score and comorbidity

Model selection and model averaging on mortality of upper gastrointestinal bleed patients

Model Selection (MS) is known to produce uncertainty into model-building process. Besides that, the process of MS is complex and time consuming. Therefore, Model Averaging (MA) had been proposed as an alternative to overcome the issues. This research will provide guidelines of obtaining best model by using two modelling approach which are Model Selection (MS) and Model Averaging (MA) and compares the performance of both methods. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were applied in the model-building using MS to help determine the best model. In MA process, model selection criteria are needed to compute the weights of each possible models. Two model selection criteria (AICcand BIC) were compared to observe which will produce model with a better performance. For guidelines illustration, data of Upper Gastrointestinal Bleed (UGIB) were explored to identify influential factors which leads to the mortality of patients. At the end of the study, best model using MA shown to have a better performance andAICc is proven to be a better model selection criterion approach in MA. In conclusion, the most significant factors for mortality of UGIB patients were identified to be shock score, comorbidity and rebleed

Comparison between best subset and lasso regression on consumer price index Malaysia

This research is aimed to determine the factors contributing to the prediction of the total Consumer Price Index (CPI) in Malaysia through model selection using two methods which are the best subset and LASSO regression. The outliers are identified using the leverage values and studentized deleted residuals while the multicollinearity variables will undergo progressive elimination identified through Variance Inflation Factor (VIF) values. Both methods were compared using the Mean Square Error of Prediction (MSE(P)) to find the best approach to display the CPI data. The model with the smallest MSE(P) will be chosen as the best model. The result showed that the MSE(P) of the best model using both the best subset regression and LASSO regression is almost the same. Therefore, the model selection using LASSO regression will be chosen as the best approach due to the simple process in identifying the best model. The best LASSO model consists of nine major categories such as food and non-alcoholic beverages (X1), alcoholic beverages and tobacco (X2), clothing and footwear (X3), transport (X7), communication (X8), recreation service and culture (X9), education (X10), restaurants and hotels (X11), miscellaneous goods and services (X12)

New model averaging on household income to examine poverty in Malaysia

Household income for Malaysian vary due to many factors as Malaysian’s are multiracial and multi religious. Malaysia is divided into two regions which are east and west Malaysia. Due to different geographical area, there’s a contrast on the development between these two regions. Even though the rate of poverty in Malaysia had been reduced yearly, the poverty rate of Malaysian still has not been eradicated. This research aims to examine the consequences of households differences (age, gender, marital status, education, activity and family size), states, region and net income to the cause of poverty among Malaysian. Model Averaging (MA) and New Model Averaging (NMA) will be compared based o

Computationally Designed Anti-LuxP DNA Aptamer Suppressed Flagellar Assembly- and Quorum Sensing-Related Gene Expression in Vibrio parahaemolyticus

(1) Background: Quorum sensing (QS) is the chemical communication between bacteria that sense chemical signals in the bacterial population to control phenotypic changes through the regulation of gene expression. The inhibition of QS has various potential applications, particularly in the prevention of bacterial infection. QS can be inhibited by targeting the LuxP, a periplasmic receptor protein that is involved in the sensing of the QS signaling molecule known as the autoinducer 2 (AI-2). The sensing of AI-2 by LuxP transduces the chemical information through the inner membrane sensor kinase LuxQ protein and activates the QS cascade. (2) Methods: An in silico approach was applied to design DNA aptamers against LuxP in this study. A method combining molecular docking and molecular dynamics simulations was used to select the oligonucleotides that bind to LuxP, which were then further characterized using isothermal titration calorimetry. Subsequently, the bioactivity of the selected aptamer was examined through comparative transcriptome analysis. (3) Results: Two aptamer candidates were identified from the ITC, which have the lowest dissociation constants (Kd) of 0.2 and 0.5 micromolar. The aptamer with the lowest Kd demonstrated QS suppression and down-regulated the flagellar-assembly-related gene expression. (4) Conclusions: This study developed an in silico approach to design an aptamer that possesses anti-QS properties