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Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis
Md. Shamim Reza,
Sabba Ruhi
Issue:
Volume 4, Issue 5, September 2015
Pages:
317-321
Received:
5 July 2015
Accepted:
17 July 2015
Published:
28 July 2015
Abstract: For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.
Abstract: For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in suc...
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Analysis of Determinants of Antenatal Care Services Utilization in Nairobi County Using Logistic Regression Model
Kennedy Sakaya Barasa,
Anthony Kibira Wanjoya,
Anthony Gichuhi Waititu
Issue:
Volume 4, Issue 5, September 2015
Pages:
322-328
Received:
14 July 2015
Accepted:
24 July 2015
Published:
1 August 2015
Abstract: Objectives: The aim of this study is to assess antenatal care service utilization and determine the factors associated with antenatal care non attendance in Nairobi County. Methods: The study used data that was collected in the county by use of questionnaires in which a total of 306 mothers participated. Data Analysis: The data was analyzed using R-software version 3.0.2, and the report was represented in form of tables. Here, Logistic regression model was used to model some of effects of the demographic and socio-economic independent variables. Results: The study found out that the independent variables, age, employment status, education level, parity and husband’s education level were the determinants of antenatal care service utilization in Nairobi County. The relationship between the covariates and antenatal care service utilization were significant at α=0.05 Conclusions: The study suggested that mothers in Nairobi County should be educated or enlightened on matters that concern antenatal health care utilization so as to increase the percentage of those mothers that attend the health facilities.
Abstract: Objectives: The aim of this study is to assess antenatal care service utilization and determine the factors associated with antenatal care non attendance in Nairobi County. Methods: The study used data that was collected in the county by use of questionnaires in which a total of 306 mothers participated. Data Analysis: The data was analyzed using R...
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Prediction Intervals for Progressive Type-II Right-Censored Order Statistics from Two Independent Sequences
M. M. Mohie El-Din,
M. S. Kotb,
W. S. Emam
Issue:
Volume 4, Issue 5, September 2015
Pages:
329-338
Received:
18 March 2015
Accepted:
29 March 2015
Published:
3 August 2015
Abstract: This article discusses the problem of predicting future progressive Type-II right censored order statistics based on progressive Type-II right-censored, ordered statistics, record values and current records that observed from the past X-sequence. Such coverage probabilities of the prediction intervals are exact and don’t depend on the sampling distribution F. Finally, a real life time data were given to breakdown the insulating fluid between electrodes which is used to illustrate the derived results.
Abstract: This article discusses the problem of predicting future progressive Type-II right censored order statistics based on progressive Type-II right-censored, ordered statistics, record values and current records that observed from the past X-sequence. Such coverage probabilities of the prediction intervals are exact and don’t depend on the sampling dist...
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Stochastic Modeling of a System with Maintenance and Replacement of Standby Subject to Inspection
R. K. Bhardwaj,
Komaldeep Kaur,
S. C. Malik
Issue:
Volume 4, Issue 5, September 2015
Pages:
339-346
Received:
2 July 2015
Accepted:
21 July 2015
Published:
5 August 2015
Abstract: The present paper develops a probabilistic model of a cold standby system considering the failure of unit in standby mode. Initially the model contains one unit in operation and another identical in cold standby mode. The unit in cold standby mode fails after passage of pre specified time and goes under inspection for feasibility check for maintenance or replacement, whereas the operative unit directly goes under repair at its failure. A single service facility available in the system handles the tasks of repair, inspection, maintenance or replacement. The replacement of unit in standby mode, at its failure, takes some time; that follows certain probability distribution. The theory of semi-Markov processes and regenerative point technique are used to develop and analyze the system model. For illustration, the results are obtained for a particular case.
Abstract: The present paper develops a probabilistic model of a cold standby system considering the failure of unit in standby mode. Initially the model contains one unit in operation and another identical in cold standby mode. The unit in cold standby mode fails after passage of pre specified time and goes under inspection for feasibility check for maintena...
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Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia
Berisha Mekayhu Gelebo,
Ayele Taye Goshu
Issue:
Volume 4, Issue 5, September 2015
Pages:
347-358
Received:
28 July 2015
Accepted:
8 August 2015
Published:
19 August 2015
Abstract: Growth of productivity is the precondition to improve the living standard of people and maintain competitiveness in the globalized economy. However, wide regional deferential in labor force implies inefficiency as whole and might affect both aggregate unemployment and national output. The basic goal of this study was to model disparity in economic activity and unemployment in Southern and Oromia Regional States of Ethiopia, by incorporating spatial effects. Population and Housing Census data for 381 districts were used. The exploratory spatial data analysis, OLS regression model, and spatial econometric models were employed. The exploratory spatial data analysis results revealed that both economic activity and unemployment rates in a given district were directly affected by those of its neighbors. Economic activity and unemployment rates for males and females also spatially depended on that of neighboring districts. Spatial autocorrelations between unemployment and economic activity rates is negative. In modeling aspect, relying on specification diagnostics and measures of fit; spatial lag model was found to be the best model for modelling both economic activity and unemployment rates. The modelling results revealed that both estimates of spatial autoregressive parameters indicated the existence of spatial spillover in economic activity and unemployment rates. Spatial lag model analysis also demonstrated that average number of persons per household, crude birth rate, female and male unemployment rate were significant factors of economic activity rates. The factors, percentage of urban population, economic inactivity rate, percentage of self-employed population, percentage of unpaid family employers, and average number of persons per household were found as being factors behind disparities in unemployment rates across regions districts. In conclusion, as expected the economic activity and unemployment variables had the nature of correlation over space. It is recommended that most effective policy mix is required for stabilizing and alleviating disparity in both economic activities and unemployment of the districts considered in the regions.
Abstract: Growth of productivity is the precondition to improve the living standard of people and maintain competitiveness in the globalized economy. However, wide regional deferential in labor force implies inefficiency as whole and might affect both aggregate unemployment and national output. The basic goal of this study was to model disparity in economic ...
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Modeling the Impact of Crude Oil Price Shocks on Some Macroeconomic Variables in Nigeria Using Garch and VAR Models
Audu Isah,
Husseini Garba Dikko,
Ejiemenu Sarah Chinyere
Issue:
Volume 4, Issue 5, September 2015
Pages:
359-367
Received:
16 July 2015
Accepted:
24 July 2015
Published:
19 August 2015
Abstract: This study investigated the impact of crude oil shocks (COP) on exchange rate (EXCHR), external reserves (EXRS), gross domestic product (GDP), inflation rate (INFL), international trade (INTR) and money supply (MSUP) in Nigeria with a quarterly data from 2000 to 2014 using GARCH and VAR models. From the analysis, all the variables were stationary at first difference with p-value less than 0.05. The presence of heteroscedasticity was found in exchange rate with most of its coefficient models being significant at 5% level and the forecasting model for exchange rate is GARCH (2, 1). Crude oil shocks did not pose significant inflationary threat to the Nigerian economy in the short run; rather, it improves the level of gross domestic product. However, external reserves and international trade were significantly affected due to the recent fall in crude oil export. Oil shocks also positively affected money supply showing that monetary policy response to oil price changes; at the same time, money supply did affect GDP. These show that a diversified economy is really needed
Abstract: This study investigated the impact of crude oil shocks (COP) on exchange rate (EXCHR), external reserves (EXRS), gross domestic product (GDP), inflation rate (INFL), international trade (INTR) and money supply (MSUP) in Nigeria with a quarterly data from 2000 to 2014 using GARCH and VAR models. From the analysis, all the variables were stationary a...
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Compare and Evaluate the Performance of Gaussian Spatial Regression Models and Skew Gaussian Spatial Regression Based on Kernel Averaged Predictors
Somayeh Shahraki Dehsoukhteh
Issue:
Volume 4, Issue 5, September 2015
Pages:
368-372
Received:
31 July 2015
Accepted:
10 August 2015
Published:
19 August 2015
Abstract: In many problems in the field of spatial statistics, when modeling the trend functions, predictors or covariates are available and the goal is to build a regression model to describe the relationship between the response and predictors. Generally, in spatial regression models, the trend function is often linear and it is assumed that the response mean is a linear function of predictor values in the same location where the response variable is observed. But, in real applications, the neighboring predictors sometimes provide valuable information about the response variable particulary when the distance between the locations is small. Having considered this subject matter, Heaton and Gelfand [6] suggested using kernel averaged predictors for modeling trend functions in which neighboring predictor information are also used. The models proposed by Heaton an Gelfand seemed to be bound by data normality. So, in many more application problems, spatial response variables follow a skew distribution. Therefore, in this article, skew Gaussian spatial regression model is studied and the performance of the model is presented and evaluated in comparison with Gaussian spatial regression models based on kernel averaged predictors using simulation studies and real examples
Abstract: In many problems in the field of spatial statistics, when modeling the trend functions, predictors or covariates are available and the goal is to build a regression model to describe the relationship between the response and predictors. Generally, in spatial regression models, the trend function is often linear and it is assumed that the response m...
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An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria
Isah Audu,
Abdullahi Usman
Issue:
Volume 4, Issue 5, September 2015
Pages:
373-388
Received:
3 August 2015
Accepted:
17 August 2015
Published:
26 August 2015
Abstract: Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spatial behavior of five (5) surface water quality parameters of some rivers/streams in Niger State of Nigeria was studied using R geostatistical package gstat, in conjunction with packages sp, rgdal, spatstat and maptools. The variograms and ordinary krigged spatial maps were generated for rainy and dry seasons. The characteristics of the best variable models; range; sill and nugget effects of each parameter were obtained. The variogram analysis indicated a high spatial coherence for E.co, Mg and TDS, whereas TCo and TH indicated a low spatial coherence. The nugget to sill ratios of experimental and linear fitted variogram models in all cases were less than 0.25 indicating that the rivers/streams water level has strong spatial coherence in both seasons. This result shows that linear model is the best for both seasons. Krigged spatial variability maps revealed that an average range of 48km variograms for dry season changes more rapidly than it does in rainy season with an average range of 4.3 km and R2 values of 0.80 to 0.92.
Abstract: Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spat...
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Identifying Factors for Marriage Breakdown at Debre Birhan Town of Ethiopia: Logistic and Survival Analysis
Issue:
Volume 4, Issue 5, September 2015
Pages:
389-395
Received:
12 August 2014
Accepted:
13 November 2014
Published:
7 September 2015
Abstract: Marriage breakdown is a condition in which partners of a marital union cease to live together especially due to divorce or separation. The main objective of this study is identifying factors for marriage breakdown. To achieve this sample of 576 respondents was taken using stratified random sampling method, during March 2012. From descriptive statistics we have seen that about 41.7% of the first marriage was broken in Debre Birhan town. A series of statistical analysis have done: factors for marriage breakdown were analyzed using binary logistic regression and time to marriage breakdown was analyzed by Cox proportional hazard model. From the binary logistic regression we have seen that being infertile, marry at age of 12-18 years (early marriage), sexual incompatibility, unfaithfulness, absence of discussion and illiterate husbands are exposed to the risk of marriage break down. From the Cox proportional hazard model we have seen that; spouses who are infertile, marry b/n 12-18 years for females, too low (<4 years) or too high (>10 years) age gap, having different religion, sexual incompatibility and unfaithfulness leads to the shorter survival time of first marriage. Finally we have recommend that Spouses should have a habit of discussion, specially on sexual issue, youth should insure that they have the potential to pursue marriage its responsibility before coming to the institution. Awareness creation and counseling service should have given about the effect of early marriage, the importance of legal- marriage, impact of religion difference of spouses and gender equality.
Abstract: Marriage breakdown is a condition in which partners of a marital union cease to live together especially due to divorce or separation. The main objective of this study is identifying factors for marriage breakdown. To achieve this sample of 576 respondents was taken using stratified random sampling method, during March 2012. From descriptive statis...
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Estimation of Population Total Using Spline Functions
Issue:
Volume 4, Issue 5, September 2015
Pages:
396-403
Received:
17 August 2015
Accepted:
29 August 2015
Published:
9 September 2015
Abstract: This study sought to estimate finite population total using spline functions. The emerging patterns from spline smoother were compared with those that were obtained from the model-based, the model-assisted and the non-parametric estimators. To measure the performance of each estimator, three aspects were considered: the average bias, the efficiency by use of the average mean square error and the robustness using the rate of change of efficiency. We used six populations: four natural and two simulated. The findings showed that the model-based estimator works very well in terms of efficiency while the model-assisted is almost unbiased when the model is linear and homoscedastic. However, the estimators break down when the underlying model assumptions are violated. The Kernel Estimator (Nadaraya-Watson) is found to be the most robust of the five estimators considered. Between the two spline functions that we considered, the periodic spline was found to perform better. The spline functions were found to provide good results whether or not the design points were uniformly spaced. We also found out that, under certain conditions, a smoothing spline estimator and a Kernel estimator are equivalent. The study recommends that both the ratio estimator and the local polynomial estimator should be used within the confines of a linear homoscedastic model. The Nadaraya-Watson and the periodic spline estimators, both of which are non-parametric, are highly robust. The Nadaraya-Watson however is even more robust than the periodic spline.
Abstract: This study sought to estimate finite population total using spline functions. The emerging patterns from spline smoother were compared with those that were obtained from the model-based, the model-assisted and the non-parametric estimators. To measure the performance of each estimator, three aspects were considered: the average bias, the efficiency...
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Determination of Infant and Child Mortality in Kenya Using Cox-Proportional Hazard Model
Daniel Mwangi Muriithi,
Dennis K. Muriithi
Issue:
Volume 4, Issue 5, September 2015
Pages:
404-413
Received:
21 August 2015
Accepted:
1 September 2015
Published:
11 September 2015
Abstract: One of the Millennium Development Goals is the reduction of infant and child mortality by two-thirds by year 2015. To achieve this goal, efforts need be concentrated at identifying cost-effective strategies as many international agencies have advocated for more resources to be directed to health sector. One way of doing this is to identify the important factors that affect infant and child mortality. This study is necessary because, Infant and child mortality is one of the most important sensitive indicators of the social economic and health status of a community. This is because more than any other age group of a population, infants and children survival depends on the socioeconomic condition of their environment. This study addresses factors affecting infant and child mortality in Kenya. The main objective of the paper is to determine the effect of socioeconomic and demographic variables on infant and child mortality. Childhood mortality from the, KDHS 2008-09 data, was analyzed in two age periods: mortality from birth to the age of 12 months, referred to as “infant mortality” and mortality from the age of 12 months to the age of 60 months, referred to as “child mortality”. Data from Kenya Demographic and Health Survey (KDHS 2008-09) was collected by use of questionnaires, after carrying out a two-stage cluster sampling design. The Cox regression survival analysis was used to compute relative risk of the socioeconomic and demographic variables, on infant and child mortality. The study revealed that the socioeconomic and demographic factors affect both infant and child mortality. The relative risks were higher for infant’s mortality as compared to child’s mortality. The place of birth has the greatest impact on infant mortality. The study recommends policy makers and programme managers in the child health sector to formulate appropriate strategies to improve the situation, of children less than five years in Kenya, by creating awareness on these factors and improving on them.
Abstract: One of the Millennium Development Goals is the reduction of infant and child mortality by two-thirds by year 2015. To achieve this goal, efforts need be concentrated at identifying cost-effective strategies as many international agencies have advocated for more resources to be directed to health sector. One way of doing this is to identify the impo...
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Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis
Akinwande Michael Olusegun,
Hussaini Garba Dikko,
Shehu Usman Gulumbe
Issue:
Volume 4, Issue 5, September 2015
Pages:
414-419
Received:
25 July 2015
Accepted:
6 August 2015
Published:
18 September 2015
Abstract: In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.
Abstract: In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting va...
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