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

A robust estimation method of location and scale with application in monitoring process variability

This thesis consists of two parts; theoretical and application. The first part proposes the development of a new method for robust estimation of location and scale, in data concentration step (C-step), of the most widely used method known as fast minimum covariance determinant (FMCD). This new method is as effective as FMCD and minimum vector variance (MVV) but with lower computational complexity. In FMCD, the optimality criterion of C-step is still quite cumbersome if the number of variables p is large because of the computation of sample generalized variance. This is the reason why MVV has been introduced. The computational complexity of the C-step in FMCD is of order ( ) 3 O p while MVV is ( ) 2 O p . This is a significant improvement especially for the case when p is large. In this case, although MVV is faster than FMCD, it is still time consuming. Thus, this is the principal motivation of this thesis, that is, to find another optimal criterion which is of far higher computational efficiency. In this study, two other different optimal criteria which will be able to reduce the running time of C-step is proposed. These criteria are (i) the covariance matrix equality and (ii) index set equality. Both criteria do not require any statistical computations, including the generalized variance in FMCD and vector variance in MVV. Since only a logical test is needed, the computational complexities of the C-step are of order ( ) ln O p p. The second part is the application of the proposed criteria in robust Phase I operation of multivariate process variability based on individual observations. Besides that, to construct a more sensitive Phase II operation, both Wilks’ W statistic and Djauhari’s F statistic are used. Both statistics have different distributions and is used to measure the effect of an additional observation on covariance structure

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

Network topology of people NEWS

The new method in a survey on public opinion called People NEWS will be highlighted. The application of NEWS which stand for needs, expectations, wants and satisfaction will be highlighted in the process of air travel in Malaysia. The passenger’s opinions according to their NEWS from the first stage process at the departure airport until the final process at the arrival airport will be discussed based on their network topology. The information from the NEWS network can be filtered by using the minimal spanning tree and the description about the behaviour of the network can be explained by using centrality measures. Some important results and recommendations based on passenger’s NEWS for Malaysian commercial flights will be highlighted

Multidimensional Minimal Spanning Tree: The Bursa Malaysia

The stock market has constituted a complex system since the interrelationships among the stocks are complicated and unpredictable. Moreover, the stock price does not stagnate at a certain price all the time but the price keeps changing from minute to minute during the transaction hours. Thus, it is quite difficult to indicate which stock influences the performances of other stocks as well as the behaviours of the stocks in a network. The economic information might be misleading and incomplete if the analysis applies with univariate time series of stock price only as each stock is represented by four features of the price. To obtain the complete information of the Bursa Malaysia stock network as well as the interrelationships among the stocks, multivariate time series of stocks are measured by using RV coefficient. Besides, minimum spanning tree and centrality measures are applied in this paper in order to construct the stock network virtually and determine the behaviours of the stocks by using the recent data of top 100 stocks in Bursa Malaysia

Monitoring process variability and root cause analysis in paper box production

In this paper, monitoring procedure for process variability in multivariate setting based on individual observations which is a combination of (i) Hotelling’s T 2 control chart in detecting out of control signal and (ii) implementation of Mason, Young and Tracy (MYT) decomposition and structure analysis technique for root cause analysis is introduced. The advantages of this procedure will be shown by using the case of a paper box production process in one of the Malaysian manufacturing companies. The successful application of this multivariate approach could act as a stimulant for most industries to imitate in process monitoring. Moreover, the computation efficiency in root cause analysis enables quality’s multiple characteristics to be monitored simultaneously. Based on the findings, the core issue that needs to be a matter of concern by the management team is the closure tap of the box. This process variation should be solved immediately to avoid the products’ quality from further deteriorating

Multidimensional minimal spanning tree: the bursa Malaysia

The stock market has constituted a complex system since the interrelationships among the stocks are complicated and unpredictable. Moreover, the stock price does not stagnate at a certain price all the time but the price keeps changing from minute to minute during the transaction hours. Thus, it is quite difficult to indicate which stock influences the performances of other stocks as well as the behaviours of the stocks in a network. The economic information might be misleading and incomplete if the analysis applies with univariate time series of stock price only as each stock is represented by four features of the price. To obtain the complete information of the Bursa Malaysia stock network as well as the interrelationships among the stocks, multivariate time series of stocks are measured by using RV coefficient. Besides, minimum spanning tree and centrality measures are applied in this paper in order to construct the stock network virtually and determine the behaviours of the stocks by using the recent data of top 100 stocks in Bursa Malaysia

Behaviours of Bursa Malaysia: a multidimensional network analysis

In current practice, the similarities between two or more univariate time series of stocks are determined by using the Pearson correlation coefficient (PCC). However, the economic information might be misleading if the analysis applies only the univariate time series of stock price, as each stock is denoted by four types of prices. Therefore, multidimensional of stocks are taken into account in this paper. The similarities between two or more multi-dimensional of stocks are quantified by using Random Vector (RV) coefficient. Additionally, an algorithm is proposed due to the computational of RV coefficient is tedious and time-consuming when a large number of stocks are included. In this paper, the Malaysian stock network analysis in univariate and multivariate setting are conducted and analysed by using the PCC, RV coefficient, forest of all possible MSTs and centrality measures. In summary, there is some important economic information could not be brought out by univariate network analysis alone

On the reliability of Shewhart-type control charts for multivariate process variability

We show that in the current practice of multivariate process variability monitoring, the reliability of Shewhart-type control charts cannot be measured except when the sub-group size n tends to infinity. However, the requirement of large n is meaningless not only in manufacturing industry where n is small but also in service industry where n is moderate. In this paper, we introduce a new definition of control limits in the two most appreciated control charts in the literature, i.e., the improved generalized variance chart (IGV-chart) and vector variance chart (VV-chart). With the new definition of control limits, the reliability of the control charts can be determined. Some important properties of new control limits will be derived and the computational technique of probability of false alarm will be delivered

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