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Estimation of the Expected Period of acquired Tuberculosis to Become a Chronic Tuberculosis
O. M. Adetutu,
L. A. Nafiu,
O. M. Adetutu,
L. A. Nafiu
Issue:
Volume 2, Issue 3, May 2013
Pages:
42-47
Received:
10 April 2013
Published:
2 May 2013
Abstract: In order to assess hazards pose by chronic tuberculosis, a table similar to a life table is prepared to estimate the expected period of the acquired tuberculosis to become chronic tuberculosis. This is ailment attributable to the Mycobacterium tuberculosis. Confidence bounds for the estimate were also derived. An example is given using a data set from University of Ilorin Teaching Hospital, Ilorin in Kwara State, Nigeria where some tuberculosis patients were monitored over some years. The available data were analyzed using Statistical Package for Social Sciences (SPSS) version 15, Chicago Inc., IL, and USA. The results show that for the potential patient(s), the expected period of developing the Tuberculosis is 6.796 years before the infection become chronic infection (Tuberculosis). Therefore, 95% confidence interval for the estimated period was found to be between 6.7763 and 6.8157. Hence, it is recommended that health policy maker should formulate policies that curb the pandemic of the disease.
Abstract: In order to assess hazards pose by chronic tuberculosis, a table similar to a life table is prepared to estimate the expected period of the acquired tuberculosis to become chronic tuberculosis. This is ailment attributable to the Mycobacterium tuberculosis. Confidence bounds for the estimate were also derived. An example is given using a data set f...
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Using the Markov Chain Monte Carlo Method to Make Inferences on Items of Data Contaminated by Missing Values,
Issue:
Volume 2, Issue 3, May 2013
Pages:
48-53
Received:
7 April 2013
Published:
2 May 2013
Abstract: The Markov Chain Monte Carlo (MCMC) is a method that is used to estimate parameters of interest under difficult conditions such as missing data or when underlying distributions do not fit the assumptions of Maximum Likelihood processes. The objective of this process is to find a probability distribution known as a posterior distribution in Bayesian analysis that can be used to estimate target parameters. In this paper, we consider a case where data are contaminated with missing values and therefore need to be adequately handled using missing data techniques before making inferences on them. A review of the mathematics involved in MCMC procedures in the presence of missing data is presented. Furthermore, we use real data to compare inferences made using multiple imputation based on the multivariate normal model (MVN) that uses the MCMC procedure, the case deletion (CD) missing data method that discards subjects with missing values from the analysis, and the fully conditional specification (FCS) multiple imputation method that uses a sequence of regression models to fill in missing values. Assuming that data are missing completely at random (MCAR) on continuous and normally distributed variables, the following findings are obtained: (1) The higher the proportion of missing data on a variable of interest, the more the relationship between that variable and the dependent variable is distorted when all missing data methods are applied. (2) Multiple imputation based methods produce similar estimates which are better than estimates from the case deletion method. (3) At some stage (when the proportion of missing data becomes high), none of the missing data techniques can help to maintain an initially existing relationship between the dependent variable and some of the covariates of interest in the dataset.
Abstract: The Markov Chain Monte Carlo (MCMC) is a method that is used to estimate parameters of interest under difficult conditions such as missing data or when underlying distributions do not fit the assumptions of Maximum Likelihood processes. The objective of this process is to find a probability distribution known as a posterior distribution in Bayesian...
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Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process
O. V. Chernoyarov,
A. V. Salnikova,
Ya. A. Kupriyanova
Issue:
Volume 2, Issue 3, May 2013
Pages:
54-60
Received:
3 May 2013
Published:
30 May 2013
Abstract: The technique of a finding of distribution functions of an absolute maximum of non-Gaussian random processes has been illustrated. On an example of Hoyt process the limiting distribution laws of its absolute maximum have been found. By methods of statistical modeling it has been established that the given asymptotic approximations ensure a satisfactory description of the true distributions over a wide range of parameter values of the random process
Abstract: The technique of a finding of distribution functions of an absolute maximum of non-Gaussian random processes has been illustrated. On an example of Hoyt process the limiting distribution laws of its absolute maximum have been found. By methods of statistical modeling it has been established that the given asymptotic approximations ensure a satisfac...
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Latent growth curve modeling of psychological well-being trajectories
M. Fátima Salgueiro,
Joana Malta
Issue:
Volume 2, Issue 3, May 2013
Pages:
61-66
Received:
23 April 2013
Published:
30 May 2013
Abstract: This Paper Proposes Modeling Trajectories of Psychological Well-being Using Latent Growth Curve models (LGCMs). The psychometric scale of the General Health Questionnaire-12 (GHQ-12) is considered. Data from the British Household Panel Survey (BHPS), from years 2003 to 2006 are used. In 1991 Graetz proposed the GHQ-12 as a multidimensional scale, containing three distinct dimensions: anxiety and depression, social dysfunction and loss of confidence. Using such scale, this paper compares a second-order LGCM for the trajectories of a latent factor (measured by these three dimensions) with a LGCM for the trajectories of an overall sum score. Conditional LGCMs are then fitted; sex, age group and perceived health status are considered as the explanatory variables of the growth trajectories. Results show that the model which considers the three dimensions of subjective well-being has a larger explaining capability than the one utilizing the subjective well-being score.
Abstract: This Paper Proposes Modeling Trajectories of Psychological Well-being Using Latent Growth Curve models (LGCMs). The psychometric scale of the General Health Questionnaire-12 (GHQ-12) is considered. Data from the British Household Panel Survey (BHPS), from years 2003 to 2006 are used. In 1991 Graetz proposed the GHQ-12 as a multidimensional scale, c...
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Noise and Signal Estimation in MRI: Two-Parametric Analysis of Rice-Distributed Data by Means of the Maximum Likelihood Approach
Tatiana V. Yakovleva,
Nicolas S. Kulberg
Issue:
Volume 2, Issue 3, May 2013
Pages:
67-80
Received:
30 April 2013
Published:
10 June 2013
Abstract: The paper’s subject is the elaboration of a new approach to image analysis on the basis of the maximum likelihood method. This approach allows to get simultaneous estimation of both the image noise and the signal within the Rician statistical model. An essential novelty and advantage of the proposed approach consists in reducing the task of solving the system of two nonlinear equations for two unknown variables to the task of calculating one variable on the basis of one equation. Solving this task is important in particular for the purposes of the magnetic-resonance images processing as well as for mining the data from any kind of images on the basis of the signal’s envelope analysis. The peculiarity of the consideration presented in this paper consists in the possibility to apply the developed theoretical technique for noise suppression algorithms’ elaboration by means of calculating not only the signal mean value but the value of the Rice distributed signal’s dispersion, as well. From the view point of the computational cost the procedure of the both parameters’ estimation by proposed technique has appeared to be not more complicated than one-parametric optimization. The present paper is accented upon the deep theoretical analysis of the maximum likelihood method for the two-parametric task in the Rician distributed image processing. As the maximum likelihood method is known to be the most precise, its developed two-parametric version can be considered both as a new effective tool to process the Rician images and as a good facility to evaluate the precision of other two-parametric techniques by means of their comparing with the technique proposed in the present paper.
Abstract: The paper’s subject is the elaboration of a new approach to image analysis on the basis of the maximum likelihood method. This approach allows to get simultaneous estimation of both the image noise and the signal within the Rician statistical model. An essential novelty and advantage of the proposed approach consists in reducing the task of solving...
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Process Optimization for Synthesis of Anti-Tuberculosis Drug Catalyzed by Fluor apatite Supported Potassium Fluoride
Younes Abrouki,
Abdelkader Anouzla,
Hayat Loukili,
Rabiaâ Lotfi,
Ahmed Rayadh,
Abdellah Bahlaoui,
Saı̈d Sebti,
Driss Zakarya,
Mohamed Zahouily
Issue:
Volume 2, Issue 3, May 2013
Pages:
81-86
Received:
29 May 2013
Published:
20 June 2013
Abstract: The optimization of the synthesis of anti-tuberculosis drug by thia-Michael addition between thiophenol and chalcone catalyzed using activated Fluorapatite supported potassium fluoride (KF/FAP) was studied using a 2 block central composite design including 4 factors (reaction time, solvent volume, catalyst weight and impregnation ratio). The high reactivity and regioelectivity of our catalyst coupled with their ease of use and reduced environmental problems makes them attractive alternatives to homogeneous basic reagents.
Abstract: The optimization of the synthesis of anti-tuberculosis drug by thia-Michael addition between thiophenol and chalcone catalyzed using activated Fluorapatite supported potassium fluoride (KF/FAP) was studied using a 2 block central composite design including 4 factors (reaction time, solvent volume, catalyst weight and impregnation ratio). The high r...
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Mathematical and Simulation of Lid Driven Cavity Flow at Different Aspect Ratios Using Single Relaxation Time Lattice Boltzmann Technique
Issue:
Volume 2, Issue 3, May 2013
Pages:
87-93
Received:
30 May 2013
Published:
20 June 2013
Abstract: In this paper we, consider a restrictions on the choice of relaxation time in single relaxation time (SRT) models, simulation of flows is generally limited base on this technique. In the current study of the SRT lattice Boltzmann equation have been used to simulate lid driven cavity flow at various Reynolds numbers (100-5000) and three aspect ratios, K=1, 1.5 and 4. The point which is vital in convergence of this technique is how the boundary conditions will be implemented. Two kinds of boundary conditions which imply no-slip and constant inlet velocity, imposed in the present work. For square cavity, results show that with increasing the Reynolds number, bottom corner vortices will grow but they won’t merge together. In this case which the aspect ratio equals four, and Reynolds number reaches over 1000, simulations predicted four primary vortices, which have not predicted by previous single relaxation time models. The results have been compared by previous multi relaxation model.
Abstract: In this paper we, consider a restrictions on the choice of relaxation time in single relaxation time (SRT) models, simulation of flows is generally limited base on this technique. In the current study of the SRT lattice Boltzmann equation have been used to simulate lid driven cavity flow at various Reynolds numbers (100-5000) and three aspect ratio...
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