bluuebunny/crossref_metadata_2025_split huggingface dataset:
Does trade openness explain the deviation of purchasing power parity and exchange rate movement?
Authors: Ang A L , Thum Y T , Sek S K 
Published: August 2021

AbstractReal exchange rate movements are important in determining a country’s competitiveness and trade flows. The real exchange rate also plays an important role in postulating a relationship between exchange rates and prices as explained in the theory of purchasing power parity (PPP). In this study, we examine the validity of PPP theory in four groups of economies (developed, developing, high, and low trade openness economies) from 1980 to 2016. Our main objective is to examine if trade openness matters in determining the validity of PPP. The study applied the panel unit-root tests to test for the PPP theory. Besides, a static panel threshold regression is applied to investigate the main determinants of real exchange rate movements besides testing the relative PPP theory. The panel unit-root tests revealed the validity of relative PPP theory in all cases. However, the threshold regression reported that the PPP condition may not hold in all regimes when a nonlinearity relationship exists. Foreign and domestic prices are influential in determining the PPP relationship.
Modeling the distribution of duration time for unhealthy air pollution events
Authors: Masseran N , Safari M A M , Hussain S I 
Published: August 2021

Abstract
The information about how long a severe unhealthy air pollution event will last is crucial for the purpose of planning a possible measure to mitigate its risk. Thus, analyzing the distribution of duration data on the past occurrences of air pollution events is important. This study analyzes the hourly data of air pollution index (API) in Klang City, Malaysia from 1997 to 2018. Air pollution duration data are determined from the period when API > 100, preceded and followed by periods when API < 100. In this study, four types of statistical distributions, namely, Exponential, Gamma, Lognormal, and Weibull are proposed as practical models. Goodness-of-fit measures are compared for each distribution to determine the best fitted one to describe the observed data. Results indicate that the Lognormal distribution provides the best fitted model among others.
The prevalence of Helicobacter pylori in referral population of Turkey
Authors: Usarov Komiljon , Ahmedov Anvarjon , Abasiyanik Mustafa Fatih , Khalif Ku Muhammad Na’im Ku , Akhmedov Abdulkasim 
Published: August 2021

Abstract
Helicobacter pylori infection is commonly associated with gastroduodenal diseases in humans, such as chronic gastritis and peptic ulcers, gastric mucosa-associated lymphoid tissue lymphoma, and even gastric cancer, which leads to high cost to society for treatment and even to death many people, when people do not know early of the infection prevalence. In this work we proposed a forecasting model to predict the infection prevalence. Based on our results society can make simple early prevention acts against the infection. The early prevention acts decrease the cost of treatment and save many people’s lives in the world.
Model fitting for Malaysian mortality rate: Comparison of Heligman-Pollard and P-splines smoothing
Authors: Edrus Robiaatul Adawiah , Siri Zailan , Haron Mohd Azmi , Safari Muhammad Aslam Mohd 
Published: August 2021

Abstract
Malaysia has been experiencing longevity risk since the last decade due to improvements of mortality rates. Longevity risk refers to the probability of a person living longer than expected. According to the Department of Statistics Malaysia, a baby born in the year 2018 is predicted to live an average life of 75 years. Since the minimum retirement age policy of 60 years had come into force in 2012, the 2018 baby would live approximately 15 more years after retirement. Therefore, this study aims to compare the Heligman-Pollard and P-splines smoothing for fitting the Malaysian mortality rate. This model fitting will give a clear picture of the mortality pattern in predicting the mortality rate accurately, especially for the baby boomer generation. The data obtained from the Department of Statistics Malaysia are split into groups of five years, from 0 to 75 years old, and time ranges from 1995 to 2018. The data set from 1995 to 2010, known as the training set is used to fit the mortality rate. After fitting the mortality rate for both methods, this study will measure the performance in the testing set from 2011 until 2018. This study uses the mean absolute percentage error (MAPE) to identify the better method to fit the Malaysian mortality rate. Based on the MAPE values, P-splines smoothing gives a relatively smaller value compared to the Heligman-Pollard. For overall performance from 1995 to 2018, P-spline smoothing has proven to fit the Malaysian data well.
Comparison of Robust Estimators for Detecting Outliers in Multivariate Datasets
Authors: Mutalib Sharifah Sakinah Syed Abd , Satari Siti Zanariah , Yusoff Wan Nur Syahidah Wan 
Published: August 2021

AbstractDetecting outliers for multivariate data is difficult and does not work by visual inspection. Mahalanobis distance (MD) has been a classical method to detect outliers in multivariate data. However, classical mean and covariance matrix in MD suffer from masking and swamping effects. Masking effects happened when outliers are not identified and swamping effects happened when inliers are identified as outliers. Hence, robust estimators have been proposed to overcome these problems. In this study, the performance of a new robust estimator named Test on Covariance (TOC) is tested and compared with other robust estimators which are Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME) and Index Set Equality (ISE). These five robust estimators’ performance is being tested on five real multivariate datasets. Brain and weight, Hawkins-Bradu Kass, Stackloss, Bushfire and Milk datasets were used as these five real datasets are well-known in most outlier detection studies. Results show that TOC has proven to be able in detecting outliers, does not have a masking effect and has the same performance as other robust estimators in all datasets.
Modelling wind direction data of Langkawi Island during Southwest monsoon in 2019 to 2020 using bivariate linear functional relationship model with von Mises distribution
Authors: Mokhtar Nurkhairany Amyra , Zubairi Yong Zulina , Hussin Abdul Ghapor , Badyalina Basri , Ghazali Ahmad Faiz , Ya’acob Fatin Farazh , Shamala Palaniappan , Kerk Lee Chang 
Published: August 2021

AbstractThe weather in Malaysia is characterised by two monsoons, namely, the southwest monsoon from May to September, and the northeast monsoon from November to March. Wind direction is essential in observing the weather patterns and global climate. In this study, our interest is on investigating the relationship of the wind direction data of Langkawi Island in Malaysia during the southwest monsoon from year 2019 to 2020. It is essential to highlight that wind direction data that is circular and this requires different statistical techniques from the techniques that are used to analyse linear data. In this paper, we model the relationship of wind direction data by using the bivariate functional relationship model with von Mises distribution. The magnificence of this model is that the existence of error terms in all variables is considered. When modelling the data, outliers of the wind direction data are identified by using thecovratiomethod that considers row deletion. The covariance matrix of the parameter estimates of the data is obtained by using the Fisher information matrix. Also, the result is supported by the Q-Q plots of the von Mises that indicate the goodness-of-fit of the wind direction data to the von Mises distribution. Then, maximum likelihood estimation is used in obtaining the parameter estimates of the data and hence, the model of the wind direction data is attained. The implications of this study provides an improved comprehension of the behaviour of wind direction and may be used for the prediction of wind energy in future.
Evaluation of generated bootstrap weight in layer perceptron for Southeast Asia visitors during COVID19 outbreak
Authors: Fatihah C O S , Hila N Z , Shaharudin S M , Tarmizi R A , Romli N 
Published: August 2021

Abstract
The pandemic COVID-19 effected the global business sector include the tourism industry. Forecasting the visitor arrival from Southeast Asia is a vital for organized the economy impact at Malaysia state, particularly during this outbreak. Neural network family has been substantial approaches in tourism and the economy. The layer perceptron is a part of the neural network model which is used to produce accurate forecasting. However, the inherent biasness in the perceptron algorithm could lead to an underfitting problem which eventually leads to poor performance of forecast accuracy. The motivation of this study is to improve the accuracy of single-layer perceptron in forecasting the Southeast Asia visitors in Malaysia during COVID19. In this study, the bootstrap weights are generated at the hidden layer to reduce the biasness in output layer. The forecasting result of generated bootstrap weight model is compared with conventional perceptron model in terms of small bias estimation. The statistical results revealed that the generated bootstrap weight in perceptron provides accurate forecasting for Southeast Asia visitors during COVID-19.
Geographically Weighted Regression on dengue epidemic in Peninsular Malaysia
Authors: Sulekan Ayuna , Suhaila Jamaludin , Wahid Nurmarni Athirah Abdul 
Published: August 2021

Abstract
Dengue has been a global epidemic since World War II, with millions of individuals being infected every year. Repetitive dengue epidemic is one of the main health problems that, due to its rapid spread and geographically widespread, has become a major concern for the government authorities in dealing with this disease. In Malaysia, cases of dengue are reported annually. To keep cases under control, it is important to examine the possible factors that help the growth of the virus. Climatological factors such as rainfall, temperature, wind speed, and humidity are expected to have high potential to increase the growth of the virus in this study, and their spatial variation is associated with cases of dengue. The result revealed that Ordinary Least Square was not an effective method for modelling the relationships between dengue cases and climate variables, as climate variables in different spatial regions act differently. During the analysis, there could be some issues of non-stationarity since the geographical aspect and spatial data were involved. Hence, the Geographically Weighted Regression (GWR) is implemented due to its capability to identify the spatial non-stationarity behavior of influencing factors on dengue incidence and integrate the geographical location and altitude for the spatial analysis. GWR analysis found that the influenced factors exhibited a significant relationship with dengue incidence. GWR also shows a significant improvement in Akaike Information Criteria (AIC) values with the lowest value and the highest adjusted R square. It is expected that the developed model can help the local hygienic authorities design better strategies for preventing and controlling this epidemic in Malaysia.
COVRATIO statistic for replicated linear functional relationship model
Authors: Mohd Arif A , Zubairi Y Z , Hussin A G 
Published: August 2021

AbstractReplicated linear functional relationship model (LFRM) can be categorized under the errors-in-variables model where variables involved are measured with error. However, the presence of outliers in dataset significantly impacts the parameter estimation. We extend the use of the COVRATIO statistic which has been successfully used in unreplicated LFRM for detecting the outliers. A simulation study is used to obtain the cut-off point at 10% upper percentiles. An illustration of this procedure is presented for its potential in a real data set. The procedure successfully identifies the outlier present in the data set.
Priors comparison in Bayesian Models of risk factor of Malaysian coronary artery disease male patients
Authors: Juhan Nurliyana , Zubairi Yong Zulina , Zuhdi Ahmad Syadi Mahmood , Khalid Zarina Mohd 
Published: August 2021

AbstractCoronary artery disease (CAD) continues to be one of the leading causes of morbidity and mortality globally. Of particular relevance for this issue is that major efforts should be focused on understanding the risk factor involved. In this study, three types of Bayesian models, each with different prior distribution were considered to identify associated risk factors in CAD among Malaysian male patients presenting with ST-Elevation Myocardial Infarction (STEMI) and to obtain a feasible model to fit the data. The results of the three models were compared to find the best model. A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate and multivariate analyses for the three models were performed using one of the Bayesian Markov Chain Monte Carlo (MCMC) simulation approach known as Gibbs sampling. Models’ performances are evaluated through overall model fit. Bayesian model C which used both Beta and Dirichlet prior distributions, consisted of six significant variables namely diabetes mellitus, family history of cardiovascular disease, chronic lung disease, renal disease, Killip class and age group was considered as the best model. The same set of variables that were observed to be significant in the Bayesian model C was also found to be significant in models A and B which used single prior distribution, respectively. Model C has a better fit than models A and B as the deviance value produced was the smallest. This study showed that posterior estimation was mostly influenced by the existing prior knowledge. Though applying the non-informative prior which were both Beta and Dirichlet distribution priors, model C can minimise uncertainty in making effective clinical decisions and provides better parameters estimates of the posterior distribution.
Effect of climate factors on Hand-Foot-Mouth Disease: A generalized additive model approach
Authors: Wahid N A A , Suhaila J , Rahman H A , Sulekan A 
Published: August 2021

AbstractHand, foot, and mouth disease (HFMD) has become an endemic childhood disease in Asia, including Malaysia, over the last few decades. This infectious disease caused by the Entero and Coxsackie viruses has been a major public health threat in Malaysia since 1997. Climate change has been considered an influential factor in HFMD cases and has been explored in other countries using various statistical analyses. The most popular is the Generalized Linear Model (GLM). However, GLM often fails to capture the non-linearity effect of the variables. The study, therefore, proposes to use the Generalized Additive Model (GAM) to analyse the non-linear effects of temperature, humidity, rainfall, and wind speed at varying time lags of HFMD in Selangor. In summary, the result indicates that the weekly temperature, humidity, and rainfall were significantly associated with HFMD cases in Selangor and clarified with two weeks of lag time. This disease’s risk increased in the subsequent two weeks with a temperature range of 27°C to 30°C, 70% to 85% of humidity, and 5mm to 20mm of rainfall. Besides, this study also found that the seasonal distribution of HFMD in Selangor has a large peak during the Southwest monsoon. A small peak was observed at the end of the year during the Northeast monsoon. The findings of this study could be a practical guide for HFMD intervention strategies, especially in Malaysia.
Piecewise linear modelling and change-point analysis of COVID-19 outbreak in Malaysia
Authors: Phang P , Abdul Taib N A , Safii R , Labadin J 
Published: August 2021

Abstract
In Malaysia, COVID-19 were first detected as imported cases on 25 January and as local infection on 4 February 2020. A surge of positive cases ensued by March 2020 which led to a series of countrywide containment and mitigation measures known as Movement Control Order (MCO). We study the direct effects of MCO on the course of epidemic by analyzing the cumulative and daily infection cases of COVID-19 up to 31 December 2020 in Malaysia and its states using piecewise linear regression and segment neighborhoods algorithm of change-point analysis, respectively. Through piecewise regression on nationwide cases, MCO were likely to almost flatten the epidemic curve in just one month after it was first initiated. While for stateswise cases, the average length of series of concave downward is six months before it turn to concave upward, indicating the period of which deceleration of new cases can be expected. However, the starting of this wave of COVID-19 can be relatively vary for three months in different states and federal territories. Together with change-point analysis on daily cases, the statewise epidemic phases could be subdivided into two to four regimes, whereby the majority of phase transitions fall in April and last quarter of 2020. Overall, the statistical modelling shows that the immediate effect of MCO appears to be effective.
Assessing the adequacy of the gompertz regression model in the presence of right censored data
Authors: Maarof Nur Niswah Naslina Azid @ , Arasan Jayanthi , Zulkafli Hani Syahida , Adam Mohd Bakri 
Published: August 2021

Abstract
The Gompertz distribution is often used to model human mortality and establish actuarial tables. Cox-Snell residual is the most general practice to evaluate the model adequacy. However, standard statistical procedures are not amenable to handle the censored observations. Therefore, this research investigates the adequacy of the two parameter Gompertz parametric survival model that was extended to incorporate with fixed covariate in the presence of right censored data. Performance of the model is assessed and compared at various combinations of sample sizes and censoring proportions via simulation study. The newly proposed modifications to the Cox-Snell residuals based on the geometric mean and harmonic mean as well as the jackknife means were compared with Cox-Snell and Modified Cox-Snell residuals via simulation studies at different settings. The log-cumulative hazard plot of residuals is obtained by plotting the proposed residuals against the cumulative hazard function to assess the model’s fit. Based on the model adequacy study, we found out that jackknife geometric mean and jackknife harmonic mean outperform CS, MCS, GMCS and HMCS residuals.
Effects of climate and irrigation on GRACE-based estimates of water storage changes in major US aquifers
Authors: Scanlon Bridget R , Rateb Ashraf , Pool Donald R , Sanford Ward , Save Himanshu , Sun Alexander , Long Di , Fuchs Brian 
Published: August 2021

Abstract
Understanding climate and human impacts on water storage is critical for sustainable water-resources management. Here we assessed climate and human drivers of total water storage (TWS) variability from Gravity Recovery and Climate Experiment (GRACE) satellites compared with drought severity and irrigation water use in 14 major aquifers in the United States. Results show that long-term variability in TWS tracked by GRACE satellites is dominated by interannual variability in most of the 14 major US aquifers. Low TWS trends in the humid eastern U.S. are linked to low drought intensity. Although irrigation pumpage in the humid Mississippi Embayment aquifer exceeded that in the semi-arid California Central Valley, a surprising lack of TWS depletion in the Mississippi Embayment aquifer is attributed to extensive streamflow capture. Marked storage depletion in the semi-arid southwestern Central Valley and south-central High Plains totaled ∼90 km3, about three times greater than the capacity of Lake Mead, the largest U.S. reservoir. Depletion in the Central Valley was driven by long-term droughts (⩽5 yr) amplified by switching from mostly surface water to groundwater irrigation. Low or slightly rising TWS trends in the northwestern (Columbia and Snake Basins) US are attributed to dampening drought impacts by mostly surface water irrigation. GRACE satellite data highlight synergies between climate and irrigation, resulting in little impact on TWS in the humid east, amplified TWS depletion in the semi-arid southwest and southcentral US, and dampened TWS deletion in the northwest and north central US Sustainable groundwater management benefits from conjunctive use of surface water and groundwater, inefficient surface water irrigation promoting groundwater recharge, efficient groundwater irrigation minimizing depletion, and increasing managed aquifer recharge. This study has important implications for sustainable water development in many regions globally.
Elevated risk of tropical cyclone precipitation and pluvial flood in Houston under global warming
Authors: Zhu Laiyin , Emanuel Kerry , Quiring Steven M 
Published: August 2021

Abstract
Pluvial floods generated by tropical cyclones (TCs) are one of the major concerns for coastal communities. Choosing Houston as an example, we demonstrate that there will be significantly elevated risk of TC rainfall and flood in the future warming world by coupling downscaled TCs from Model Intercomparison Project Phase 6 models with physical hydrological models. We find that slower TC translation speed, more frequent stalling, greater TC frequency, and increased rain rate are major contributors to increased TC rainfall risk and flood risk. The TC flood risk increases more than the rainfall. Smaller watersheds with a high degree of urbanization are particularly vulnerable to future changes in TC floods in a warming world.
A vital option for food security and greenhouse gases mitigation: planting elite super rice in double- to single-rice cropping fields in China
Authors: Fan Dongli , Fan Yidan , Tian Zhan , Li Xiubin , Jiang Min , Sun Laixiang , Zhong Honglin , Wang Kai , Wang Xiangyi , Jiang Luguang 
Published: August 2021

Abstract
Double-rice cropping (DRC) in southern China has made outstanding contributions to ensuring food security, along with a large amount of greenhouse gas (GHG) emissions. The observed significant shift from double- to single-rice (DtS) cropping since 1990 in southern China has led to great concerns on food security, despite its contribution to GHG emissions reduction. How to ensure food security without compromising the goal of mitigating GHG emission requires innovative thinking and a comprehensive tradeoff analysis of all plausible options. This study adopts a multi-model coupling method to simulate the yield and GHG emissions trade-offs across grid-cells by incorporating the option of planting elite super rice in the DtS areas. The simulation results indicate that planting elite super rice with longer growth period in the DtS areas has the potential to compensate the annual yield loss caused by the DtS shift while significantly mitigating GHG emissions in comparison with the conventional DRC. In more detail, while the yield and GHG emissions of prevailing single-rice cropping are 48% (±2%) and 54% (±4%) lower than the corresponding (two-season sums) figures of conventional DRC under the current irrigation practice of midseason drainage, the yield and GHG emissions of super rice are 15% (±4%) and 44% (±6%) lower than the above reference figures if the emerging irrigation regime of alternate wetting and drying is adopted. Furthermore, our modeling simulations demonstrate the feasibility of promoting elite super rice cultivars across southern China. The research suggests a viable option for China to balance the trade-off between food security and GHG mitigation.