Displaying publications 61 - 80 of 93 in total

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  1. Fulltext Comparison between two fast threshold strategies: SPARK and SITA in normal subjects
    Foo SK, Cubbidge RP, Heitmar R
    Eur J Ophthalmol, 2021 Jul;31(4):1870-1876.
    PMID: 32468855 DOI: 10.1177/1120672120926455
    BACKGROUND: Numerous fast threshold strategies have been developed in perimetry which use maximum likelihood approaches to estimate the threshold. A recent approach to threshold estimation has been developed estimating the threshold from a limited number of test points which further reduces examination time. This strategy, SPARK, has not been compared to the SITA strategy. The aim of this study was to compare SPARK with SITA in a normal cohort to evaluate within and between strategy agreement in threshold estimates.

    METHODS: A total of 83 normal subjects each underwent two visual field examinations with SITA and SPARK on two separate occasions on a randomly selected eye. The eye examined and the order of strategy examined first was randomised but remained constant over the two perimetry visits.

    RESULTS: Visual field examination with SPARK Precision was on average 33% faster than SITA Standard. A positive correlation between group mean sensitivities of SITA Standard and SPARK Precision (rho = 0.713, p 

    Matched MeSH terms: Likelihood Functions
  2. Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature
    Althuwaynee OF, Pokharel B, Aydda A, Balogun AL, Kim SW, Park HJ
    J Expo Sci Environ Epidemiol, 2021 07;31(4):709-726.
    PMID: 33159165 DOI: 10.1038/s41370-020-00271-8
    Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.
    Matched MeSH terms: Likelihood Functions
  3. Comprehensive multi-locus phylogeny of Old World tree frogs (Anura: Rhacophoridae) reveals taxonomic uncertainties and potential cases of over- and underestimation of species diversity
    Chan KO, Grismer LL, Brown RM
    Mol Phylogenet Evol, 2018 10;127:1010-1019.
    PMID: 30030179 DOI: 10.1016/j.ympev.2018.07.005
    The family Rhacophoridae is one of the most diverse amphibian families in Asia, for which taxonomic understanding is rapidly-expanding, with new species being described steadily, and at increasingly finer genetic resolution. Distance-based methods frequently have been used to justify or at least to bolster the recognition of new species, particularly in complexes of "cryptic" species where obvious morphological differentiation does not accompany speciation. However, there is no universally-accepted threshold to distinguish intra- from interspecific genetic divergence. Moreover, indiscriminant use of divergence thresholds to delimit species can result in over- or underestimation of species diversity. To explore the range of variation in application of divergence scales, and to provide a family-wide assessment of species-level diversity in Old-World treefrogs (family Rhacophoridae), we assembled the most comprehensive multi-locus phylogeny to date, including all 18 genera and approximately 247 described species (∼60% coverage). We then used the Automatic Barcode Gap Discovery (ABGD) method to obtain different species-delimitation schemes over a range of prior intraspecific divergence limits to assess the consistency of divergence thresholds used to demarcate current species boundaries. The species-rich phylogeny was able to identify a number of taxonomic errors, namely the incorrect generic placement of Chiromantis inexpectatus, which we now move to the genus Feihyla, and the specific identity of Rhacophorus bipunctatus from Peninsular Malaysia, which we tentatively reassign to R. rhodopus. The ABGD analysis demonstrated overlap between intra- and interspecific divergence limits: genetic thresholds used in some studies to synonymize taxa have frequently been used in other studies to justify the recognition of new species. This analysis also highlighted numerous groups that could potentially be split or lumped, which we earmark for future examination. Our large-scale and en bloc approach to species-level phylogenetic systematics contributes to the resolution of taxonomic uncertainties, reveals possible new species, and identifies numerous groups that require critical examination. Overall, we demonstrate that the taxonomy and evolutionary history of Old-World tree frogs are far from resolved, stable or adequately characterized at the level of genus, species, and/or population.
    Matched MeSH terms: Likelihood Functions
  4. Fulltext Norm-dist Monte-Carlo integrative method for the improvement of fuzzy analytic hierarchy process
    Wicaksono FD, Arshad YB, Sihombing H
    Heliyon, 2020 Apr;6(4):e03607.
    PMID: 32346625 DOI: 10.1016/j.heliyon.2020.e03607
    This paper presents the novel approach of the Norm-dist Monte-Carlo fuzzy analytic hierarchy process (NMCFAHP) to incorporate probabilistic and epistemic uncertainty due to human's judgment vagueness in multi-criteria decision analysis. Normal distribution is applied as the most appropriate distribution model to approximate the probability distribution function of the criteria and alternatives within Monte-Carlo simulation. To test the applicability of the proposed NMCFAHP, the case study of non-destructive test (NDT) technology selection is performed in the Petroleum Company in Borneo, Indonesia. When compared with the conventional triangular fuzzy-AHP, the proposed NMCFAHP method reduces the standard error of mean values by 90.4-99.8% at the final evaluation scores. This means that the proposed NMCFAHP significantly involves fewer errors when dealing with fuzzy uncertainty and stochastic randomness. The proposed NMCFAHP delivers reliable performance to overcome probabilistic uncertainty and epistemic vagueness in the group decision making process.
    Matched MeSH terms: Likelihood Functions
  5. Fulltext Utilization of a Mixture Cure Rate Model based on the Generalized Modified Weibull Distribution for the Analysis of Leukemia Patients
    Omer ME, Abu Bakar M, Adam M, Mustafa M
    Asian Pac J Cancer Prev, 2021 Apr 01;22(4):1045-1053.
    PMID: 33906295 DOI: 10.31557/APJCP.2021.22.4.1045
    OBJECTIVE: Cure rate models are survival models, commonly applied to model survival data with a cured fraction. In the existence of a cure rate, if the distribution of survival times for susceptible patients is specified, researchers usually prefer cure models to parametric models. Different distributions can be assumed for the survival times, for instance, generalized modified Weibull (GMW), exponentiated Weibull (EW), and log-beta Weibull. The purpose of this study is to select the best distribution for uncured patients' survival times by comparing the mixture cure models based on the GMW distribution and its particular cases.

    MATERIALS AND METHODS: A data set of 91 patients with high-risk acute lymphoblastic leukemia (ALL) followed for five years from 1982 to 1987 was chosen for fitting the mixture cure model. We used the maximum likelihood estimation technique via R software 3.6.2 to obtain the estimates for parameters of the proposed model in the existence of cure rate, censored data, and covariates. For the best model choice, the Akaike information criterion (AIC) was implemented.

    RESULTS: After comparing different parametric models fitted to the data, including or excluding cure fraction, without covariates, the smallest AIC values were obtained by the EW and the GMW distributions, (953.31/969.35) and (955.84/975.99), respectively. Besides, assuming a mixture cure model based on GMW with covariates, an estimated ratio between cure fractions for allogeneic and autologous bone marrow transplant groups (and its 95% confidence intervals) were 1.42972 (95% CI: 1.18614 - 1.72955).

    CONCLUSION: The results of this study reveal that the EW and the GMW distributions are the best choices for the survival times of Leukemia patients.
    .

    Matched MeSH terms: Likelihood Functions
  6. Factors associated with the enactment of safety belt and motorcycle helmet laws
    Law TH, Noland RB, Evans AW
    Risk Anal, 2013 Jul;33(7):1367-78.
    PMID: 23106188 DOI: 10.1111/j.1539-6924.2012.01916.x
    It has been shown that road safety laws, such as motorcycle helmet and safety belt laws, have a significant effect in reducing road fatalities. Although an expanding body of literature has documented the effects of these laws on road safety, it remains unclear which factors influence the likelihood that these laws are enacted. This study attempts to identify the factors that influence the decision to enact safety belt and motorcycle helmet laws. Using panel data from 31 countries between 1963 and 2002, our results reveal that increased democracy, education level, per capita income, political stability, and more equitable income distribution within a country are associated with the enactment of road safety laws.
    Matched MeSH terms: Likelihood Functions
  7. Fulltext Multilocus phylogenetic analyses reveal unexpected abundant diversity and significant disjunct distribution pattern of the Hedgehog Mushrooms (Hydnum L.)
    Feng B, Wang XH, Ratkowsky D, Gates G, Lee SS, Grebenc T, et al.
    Sci Rep, 2016 May 06;6:25586.
    PMID: 27151256 DOI: 10.1038/srep25586
    Hydnum is a fungal genus proposed by Linnaeus in the early time of modern taxonomy. It contains several ectomycorrhizal species which are commonly consumed worldwide. However, Hydnum is one of the most understudied fungal genera, especially from a molecular phylogenetic view. In this study, we extensively gathered specimens of Hydnum from Asia, Europe, America and Australasia, and analyzed them by using sequences of four gene fragments (ITS, nrLSU, tef1α and rpb1). Our phylogenetic analyses recognized at least 31 phylogenetic species within Hydnum, 15 of which were reported for the first time. Most Australasian species were recognized as strongly divergent old relics, but recent migration between Australasia and the Northern Hemisphere was also detected. Within the Northern Hemisphere, frequent historical biota exchanges between the Old World and the New World via both the North Atlantic Land Bridge and the Bering Land Bridge could be elucidated. Our study also revealed that most Hydnum species found in subalpine areas of the Hengduan Mountains in southwestern China occur in northeastern/northern China and Europe, indicating that the composition of the mycobiota in the Hengduan Mountains reigion is more complicated than what we have known before.
    Matched MeSH terms: Likelihood Functions
  8. Fulltext A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2
    Zanti M, O'Mahony DG, Parsons MT, Li H, Dennis J, Aittomäkkiki K, et al.
    Hum Mutat, 2023;2023.
    PMID: 38725546 DOI: 10.1155/2023/9961341
    A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.
    Matched MeSH terms: Likelihood Functions
  9. Fulltext Reliability measurement for mixed mode failures of 33/11 kilovolt electric power distribution stations
    Alwan FM, Baharum A, Hassan GS
    PLoS One, 2013;8(8):e69716.
    PMID: 23936346 DOI: 10.1371/journal.pone.0069716
    The reliability of the electrical distribution system is a contemporary research field due to diverse applications of electricity in everyday life and diverse industries. However a few research papers exist in literature. This paper proposes a methodology for assessing the reliability of 33/11 Kilovolt high-power stations based on average time between failures. The objective of this paper is to find the optimal fit for the failure data via time between failures. We determine the parameter estimation for all components of the station. We also estimate the reliability value of each component and the reliability value of the system as a whole. The best fitting distribution for the time between failures is a three parameter Dagum distribution with a scale parameter [Formula: see text] and shape parameters [Formula: see text] and [Formula: see text]. Our analysis reveals that the reliability value decreased by 38.2% in each 30 days. We believe that the current paper is the first to address this issue and its analysis. Thus, the results obtained in this research reflect its originality. We also suggest the practicality of using these results for power systems for both the maintenance of power systems models and preventive maintenance models.
    Matched MeSH terms: Likelihood Functions
  10. Monsoonal differences and probability distribution of PM(10) concentration
    Md Yusof NF, Ramli NA, Yahaya AS, Sansuddin N, Ghazali NA, Al Madhoun W
    Environ Monit Assess, 2010 Apr;163(1-4):655-67.
    PMID: 19365611 DOI: 10.1007/s10661-009-0866-0
    There are many factors that influence PM(10) concentration in the atmosphere. This paper will look at the PM(10) concentration in relation with the wet season (north east monsoon) and dry season (south west monsoon) in Seberang Perai, Malaysia from the year 2000 to 2004. It is expected that PM(10) will reach the peak during south west monsoon as the weather during this season becomes dry and this study has proved that the highest PM(10) concentrations in 2000 to 2004 were recorded in this monsoon. Two probability distributions using Weibull and lognormal were used to model the PM(10) concentration. The best model used for prediction was selected based on performance indicators. Lognormal distribution represents the data better than Weibull distribution model for 2000, 2001, and 2002. However, for 2003 and 2004, Weibull distribution represents better than the lognormal distribution. The proposed distributions were successfully used for estimation of exceedences and predicting the return periods of the sequence year.
    Matched MeSH terms: Likelihood Functions
  11. Modelling Asthma Cases by Count Analysis Approach: Poisson INGARCH and Negative Binomial INGARCH
    'Aaishah Radziah Jamaludin, Fadhilah Yusof, Suhartono
    MATEMATIKA, 2020;36(1):15-30.
    MyJurnal
    Johor Bahru with its rapid development where pollution is an issue that needs to be considered because it has contributed to the number of asthma cases in this area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis was conducted since the outbreak in the asthma data for the period of July 2012 to July 2013. This occurs perhaps due to the extremely bad haze in Johor Bahru from Indonesian fires. The estimation of the parameter will be done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB-INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru.
    Matched MeSH terms: Likelihood Functions
  12. Fulltext Demographic and Habitual Factors of Periodontal Disease among South Indian Adults
    Selvaraj S, Naing NN, Wan-Arfah N, de Abreu MHNG
    Int J Environ Res Public Health, 2021 07 26;18(15).
    PMID: 34360201 DOI: 10.3390/ijerph18157910
    The aim of this study was to evaluate the performance of a set of sociodemographic and habits measures on estimating periodontal disease among south Indian adults. This cross-sectional study was carried out among 288 individuals above 18 years old in Tamil Nadu, India. The outcome of the study was periodontal disease, measured by WHO criteria. The covariates were age, ethnicity, smoking and alcohol habit. The assessment of factors predicting periodontal disease was carried out by multiple logistic regression analysis using R version 3.6.1. The demographic factors like age group (AOR = 3.56; 95% CI 1.69-7.85), ethnicity (AOR = 6.07; 95% CI 2.27-18.37), non-alcoholic (AOR = 0.31; 95% CI 0.13-0.64) and non-smoking (AOR = 0.33; 95% CI 0.15-0.67) were found to be associated with the outcome. The maximum log likelihood estimate value was -30.5 and AIC was 385 for the final model, respectively. Receiver operating characteristic (ROC) curve for the periodontal disease was 0.737. We can conclude that sociodemographic factors and habits were useful for predicting periodontal diseases.
    Matched MeSH terms: Likelihood Functions
  13. Fulltext Determinants of the willingness to participate in biobanking among Malaysian stakeholders in the Klang Valley
    Amin L, Hashim H, Mahadi Z, Ismail K
    BMC Med Res Methodol, 2018 12 05;18(1):163.
    PMID: 30518344 DOI: 10.1186/s12874-018-0619-2
    BACKGROUND: The demand in biobanking for the collection and maintenance of biological specimens and personal data from civilians to improve the prevention, diagnosis and treatment of diseases has increased notably. Despite the advancement, certain issues, specifically those related to privacy and data protection, have been critically discussed. The purposes of this study are to assess the willingness of stakeholders to participate in biobanking and to determine its predictors.

    METHODS: A survey of 469 respondents from various stakeholder groups in the Klang Valley region of Malaysia was carried out. Based on previous research, a multi-dimensional instrument measuring willingness to participate in biobanking, and its predictors, was constructed and validated. A single step Structural Equation Modelling was performed to analyse the measurements and structural model using the International Business Machines Corporation Software Package for Social Sciences, Analysis of Moment Structures (IBM SPSS Amos) version 20 with a maximum likelihood function.

    RESULTS: Malaysian stakeholders in the Klang Valley were found to be cautious of biobanks. Although they perceived the biobanks as moderately beneficial (mean score of 4.65) and were moderately willing to participate in biobanking (mean score of 4.10), they professed moderate concern about data and specimen protection issues (mean score of 4.33). Willingness to participate in biobanking was predominantly determined by four direct predictors: specific application-linked perceptions of their benefits (β = 0.35, p 
    Matched MeSH terms: Likelihood Functions
  14. A Comparison Between Bayesian and Maximum Likelihood Estimations in Estimating Finite Mixture Model for Financial Data
    Seuk-Yen Phoong, Mohd Tahir Ismail
    Sains Malaysiana, 2015;44:1033-1039.
    Over the years, maximum likelihood estimation and Bayesian method became popular statistical tools in which applied to fit finite mixture model. These trends begin with the advent of computer technology during the last decades. Moreover, the asymptotic properties for both statistical methods also act as one of the main reasons that boost the popularity of the methods. The difference between these two approaches is that the parameters for maximum likelihood estimation are fixed, but unknown meanwhile the parameters for Bayesian method act as random variables with known prior distributions. In the present paper, both the maximum likelihood estimation and Bayesian method are applied to investigate the relationship between exchange rate and the rubber price for Malaysia, Thailand, Philippines and Indonesia. In order to identify the most plausible method between Bayesian method and maximum likelihood estimation of time series data, Akaike Information Criterion and Bayesian Information Criterion are adopted in this paper. The result depicts that the Bayesian method performs better than maximum likelihood estimation on financial data.
    Matched MeSH terms: Likelihood Functions
  15. Spatial analysis of infant mortality in peninsular malaysia over three decades using mixture models
    Nuzlinda Abdul Rahman, Abdul Aziz Jemain
    Sains Malaysiana, 2013;42:1003-1010.
    Infant mortality is one of the central public issues in most of the developing countries. In Malaysia, the infant mortality rates have improved at the national level over the last few decades. However, the issue concerned is whether the improvement is uniformly distributed throughout the country. The aim of this study was to investigate the geographical distribution of infant mortality in Peninsular Malaysia from the year 1970 to 2000 using a technique known as disease mapping. It is assumed that the random variable of infant mortality cases comes from Poisson distribution. Mixture models were used to find the number of optimum components/groups for infant mortality data for every district in Peninsular Malaysia. Every component is assumed to have the same distribution, but different parameters. The number of optimum components were obtained by maximum likelihood approach via the EM algorithm. Bayes theorem was used to determine the probability of belonging to each district in every components of the mixture distribution. Each district was assigned to the component that had the highest posterior probability of belonging. The results obtained were visually presented in maps. The analysis showed that in the early year of 1970, the spatial heterogeneity effect was more prominent; however, towards the end of 1990, this pattern tended to disappear. The reduction in the spatial heterogeneity effect in infant mortality data indicated that the provisions of health services throughout the Peninsular Malaysia have improved over the period of the study, particularly towards the year 2000.
    Matched MeSH terms: Likelihood Functions
  16. Fulltext The Malacca Strait separates distinct faunas of poorly-flying Cautires net-winged beetles
    Jiruskova A, Motyka M, Bocek M, Bocak L
    PeerJ, 2019;7:e6511.
    PMID: 30863675 DOI: 10.7717/peerj.6511
    We investigated the spatial and temporal patterns of Cautires diversification on the Malay Peninsula and Sumatra to understand if the narrow and frequently dry Malacca Strait separates different faunas. Moreover, we analyzed the origin of Cautires in Malayan and Sumatran mountains. We sampled 18 localities and present the mtDNA-based phylogeny of 76 species represented by 388 individuals. The phylogenetic tree was dated using mtDNA evolution rates and the ancestral ranges were estimated using the maximum likelihood approach. The phylogeny identified multiple lineages on the Malay Peninsula since the Upper Eocene (35 million years ago, mya) and a delayed evolution of diversity in Sumatra since the Upper Oligocene (26 mya). A limited number of colonization events across the Malacca Strait was identified up to the Pliocene and more intensive faunal exchange since the Pleistocene. The early colonization events were commonly followed by in situ diversification. As a result, the Malacca Strait now separates two faunas with a high species-level turnover. The montane fauna diversified in a limited space and seldom took part in colonization events across the Strait. Besides isolation by open sea or a savannah corridor, mimetic patterns could decrease the colonization capacity of Cautires. The Malay fauna is phylogenetically more diverse and has a higher value if conservation priorities should be defined.
    Matched MeSH terms: Likelihood Functions
  17. Fulltext Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
    Zafar R, Dass SC, Malik AS
    PLoS One, 2017;12(5):e0178410.
    PMID: 28558002 DOI: 10.1371/journal.pone.0178410
    Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.
    Matched MeSH terms: Likelihood Functions
  18. Fulltext Hypothesis tests of Goodness-of-Fit for Fréchet distribution
    Abidin, N. Z., Adam, M. B., Midi, H.
    Pertanika Journal of Science & Technology, 2014;22(2):419-432.
    MyJurnal
    Extreme Value Theory (EVT) is a statistical field whose main focus is to investigate extreme phenomena. In EVT, Fréchet distribution is one of the extreme value distributions and it is used to model extreme events. The degree of fit between the model and the observed values was measured by Goodness-of-fit (GOF) test. Several types of GOF tests were also compared. The tests involved were Anderson-Darling (AD), Cramer-von Mises (CVM), Zhang Anderson Darling (ZAD), Zhang Cramer von-Mises (ZCVM) and Ln. The values of parameters μ, σ and ξ were estimated by Maximum Likelihood. The critical values were developed by Monte-Carlo simulation. In power study, the reliability of critical values was determined. Besides, it is of interest to identify which GOF test is superior to the other tests for Fréchet distribution. Thus, the comparisons of rejection rates were observed at different significance levels, as well as different sample sizes, based on several alternative distributions. Overall, given by Maximum Likelihood Estimation of Fréchet distribution, the ZAD and ZCVM tests are the most powerful tests for smaller sample size (ZAD for significance levels 0.05 and 0.1, ZCVM for significance level 0.01) as compared to AD, which is more powerful for larger sample size.
    Matched MeSH terms: Likelihood Functions
  19. Fulltext Models and theories of prescribing decisions: A review and suggested a new model
    Murshid MA, Mohaidin Z
    Pharm Pract (Granada), 2017 Apr-Jun;15(2):990.
    PMID: 28690701 DOI: 10.18549/PharmPract.2017.02.990
    To date, research on the prescribing decisions of physician lacks sound theoretical foundations. In fact, drug prescribing by doctors is a complex phenomenon influenced by various factors. Most of the existing studies in the area of drug prescription explain the process of decision-making by physicians via the exploratory approach rather than theoretical. Therefore, this review is an attempt to suggest a value conceptual model that explains the theoretical linkages existing between marketing efforts, patient and pharmacist and physician decision to prescribe the drugs. The paper follows an inclusive review approach and applies the previous theoretical models of prescribing behaviour to identify the relational factors. More specifically, the report identifies and uses several valuable perspectives such as the 'persuasion theory - elaboration likelihood model', the stimuli-response marketing model', the 'agency theory', the theory of planned behaviour,' and 'social power theory,' in developing an innovative conceptual paradigm. Based on the combination of existing methods and previous models, this paper suggests a new conceptual model of the physician decision-making process. This unique model has the potential for use in further research.
    Matched MeSH terms: Likelihood Functions
  20. Energy innovations and environmental sustainability in the U.S.: The roles of immigration and economic expansion using a maximum likelihood method
    Solarin SA, Bello MO
    Sci Total Environ, 2020 Apr 10;712:135594.
    PMID: 31787295 DOI: 10.1016/j.scitotenv.2019.135594
    Environmental degradation remains a huge obstacle to sustainable development. Research on the factors that promote or degrade the environment has been extensively conducted. However, one important variable that has conspicuously received very limited attention is energy innovations. To address this gap in the literature, this study investigated the effects of energy innovations on environmental quality in the U.S. for the period 1974 to 2016. We have incorporated GDP and immigration as additional regressors. Three indices comprising of CO2 emissions, ecological footprint and carbon footprint were used to proxy environmental degradation. The cointegration tests established long-run relationships between the variables. Using a maximum likelihood approach with a break, the results showed evidence that energy innovations significantly improve environmental quality while GDP degrades the quality of the environment, and immigration has no significant effect on the environment. Policy implications of the results are discussed in the body of the manuscript.
    Matched MeSH terms: Likelihood Functions
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