Mohd Bakri Adam

Impact of Physical Activity on Cognitive Decline, Dementia, and Its Subtypes: Meta-Analysis of Prospective Studies

The association of physical activity with dementia and its subtypes has remained controversial in the literature and has continued to be a subject of debate among researchers. A systematic review and meta-analysis of longitudinal studies on the relationship between physical activity and the risk of cognitive decline, all-cause dementia, Alzheimers disease, and vascular dementia among nondemented subjects are considered. A comprehensive literature search in all available databases was conducted up until April 2016. Well-defined inclusion and exclusion criteria were developed with focus on prospective studies ≥ 12 months. The overall sample from all studies is 117410 with the highest follow-up of 28 years. The analyses are performed with both Bayesian parametric and nonparametric models. Our analysis reveals a protective effect for high physical activity on all-cause dementia, odds ratio of 0.79, 95% CI (0.69, 0.88), a higher and better protective effect for Alzheimers disease, odds ratio of 0.62, 95% CI (0.49, 0.75), cognitive decline odds ratio of 0.67, 95% CI (0.55, 0.78), and a nonprotective effect for vascular dementia of 0.92, 95% CI (0.62, 1.30). Our findings suggest that physical activity is more protective against Alzheimers disease than it is for all-cause dementia, vascular dementia, and cognitive decline.

Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data

Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.

Bivariate Extreme Analysis of Olympic Swimming Data

We model the times of the gold medalist swimmers in the Olympic Games. As the data represent an extreme value we use methods from extreme value theory. Features of the recorded variables lead to the inclusion of mixed parametric and nonparametric modeling for the marginal nonstationarity, constraints on marginal parameters to account for stochastic ordering between times from different events, and bivariate modeling to capture dependence across winning event times. Our analysis provides greater insight into the progression of winning times.

Slice sampling technique in Bayesian extreme of gold price modelling

In this paper, a simulation study of Bayesian extreme values by using Markov Chain Monte Carlo via slice sampling algorithm is implemented. We compared the accuracy of slice sampling with other methods for a Gumbel model. This study revealed that slice sampling algorithm offers more accurate and closer estimates with less RMSE than other methods . Finally we successfully employed this procedure to estimate the parameters of Malaysia extreme gold price from 2000 to 2011.

Assessing the performance of compound smoother in extracting sine signal from heavy noise

The 4253H (Twice) is a type of established non-linear compound smoother that has the capability to extract signal from heavy noise. In this paper, the performance of 4253H (Twice) in recovering the sine signal with the existence of four different percentage of contaminated error were assessed. The performances are measured by the mean of least square coefficient linear regression of the smoothed sequence on the signal sequence and Estimated Integrated Mean Square Error via simulation process. The 4253H (Twice) performs best in extracting the signal at low frequency. At the present of 75% contaminated error, this method manages to recover the signal fairly well. Therefore, 4253H (Twice) is an effective method in smoothing the heavy noise data.

Modification of smoothing in 4253H[T]

Some modified non-linear smoothers particularly 4253H[T] are explained in this paper. The modifications are focused on estimating the middle point of running median for even span by applying the following types of means; geometric, harmonic, quadratic and contraharmonic. The performance of the techniques is assessed by applying it to daily price index of a bank in Malaysia that issues sukuk for funding in Islamic banking and financial business. The results show that 4253H[T] with geometric mean modification is better than others in preserving variation and curve fitting.

Modified boxplot for extreme data

A boxplot is an exploratory data analysis (EDA) tool for a compact distributional summary of a data set. It is designed to captures all typical observations and displays the location, spread, skewness and the tail of the data. The precision of some of this functionality is considered to be more reliable for symmetric data type and thus less appropriate for skewed data such as the extreme data. Many observations from extreme data were erroneously marked as outliers by the Tukeys standard boxplot. We proposed a modified boxplot fence adjustment using the Bowley coefficient, a robust skewness measure. The adjustment will enable us to detect inconsistent observations without any parametric assumption about the distribution of the data. The new boxplot is capable of displaying some additional features such as the location parameter region of the Gumbel fitted extreme data. A simulated and real life data were used to show the advantages of this development over those found in the literature.

Joint modelling of longitudinal 3MS scores and the risk of mortality among cognitively impaired individuals

Background Modified Mini-Mental State Examination (3MS) is an instrument administered by trained personnel to examine levels of participants’ cognitive function. However, the association between changes in scores over time and the risk of death (mortality) is not known. The aims of this study are to examine the association between 3MS scores and mortality via cognitive impairment among older women and to determine individuals’ risk of changes in scores to better predict their survival and mortality rates. Methods We propose a Bayesian joint modelling approach to determine mortality due to cognitive impairment via repeated measures of 3MS scores trajectories over a 21-year follow-up period. Data for this study are taken from the Osteoporotic Fracture longitudinal study among women aged 65+ which started in 1986–88. Results The standard relative risk model from the analyses with a baseline 3MS score after adjusting for all the significant covariates demonstrates that, every unit decrease in a 3MS score corresponds to a non-significant 1.059 increase risk of mortality with a 95% CI of (0.981, 1.143), while the extended model results in a significant 0.09% increased risk in mortality. The joint modelling approach found a strong association between the 3MS scores and the risk of mortality, such that, every unit decrease in 3MS scores results in a 1.135 (13%) increased risk of death via cognitive impairment with a 95% CI of (1.056, 1.215). Conclusion It has been demonstrated that a decrease in 3MS results has a significant increase risk of mortality due to cognitive impairment via joint modelling, but insignificant when considered under the standard relative risk approach.

Model selection and validation of extreme distribution by goodness-of-fit test based on conditional position

In Extreme Value Theory, the important aspect of model extrapolation is to model the extreme behavior. This is because the choice of the extreme value distribution model affects the prediction that is about to be made. Thus, model validation which is called Goodness-of-fit (GoF) test is necessary. In this study, the GoF tests were used to fit the Generalized Extreme Value (GEV) Type-II model against the simulated observed values. The μ, σ and ξ were estimated by Maximum Likelihood. The critical values based on conditional points were developed by Monte-Carlo simulation. The powers of the tests were identified by power study. The data that is distributed according to GEV Type-II distribution was used to test whether the critical values developed are able to confirm the fit between GEV Type-II model and the data. To confirm the fit, the statistics value of the GOF test should be smaller than the critical value.

Extreme Value Theory for Modeling and Prediction of High PM10 Concentration in Johor

When consider the extreme level of pollutant concentration, the Extreme Value Theory (EVT) is a best solution to model the extreme data and predict the level of dangerous concentration. This article analyzes the extreme PM10 concentration monitored at three monitoring stations in Johor. The diagnostic plots show that the GEV distribution of EVT with Frechet type is well fitted for modeling the monthly maximum PM10 concentration during the years 2001–2010, therefore, is sufficient for prediction. The application of EVT in air quality study is concerned on how well the mathematical theory further answer the question relating to the probability that the pollutant concentration will exceed a certain level in a period. In EVT, this quantity is often called return levels. The 10, 20 and 100-year return level is computed for future prediction. It is expected that the Muar station have high PM10 return level since it is located across the Malacca Strait from Sumatra, which is closest to the hot spots. The predicted return levels suggest that the intensity of coming pollution events for PM10 will worse in the future.