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Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches
Issue:
Volume 6, Issue 6, November 2017
Pages:
254-269
Received:
14 September 2017
Accepted:
25 September 2017
Published:
3 November 2017
Abstract: The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.
Abstract: The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employe...
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Impact of Measurement Errors on Estimators of Parameters of a Finite Population with Linear Trend Under Systematic Sampling
Oloo Odhiambo Erick,
James Kahiri,
Wafula Mike Erick
Issue:
Volume 6, Issue 6, November 2017
Pages:
270-277
Received:
17 September 2017
Accepted:
4 October 2017
Published:
10 November 2017
Abstract: The study involves investigating the impact of measurement errors on estimators of parameters of a finite population with linear trend among population values, under systematic sampling. The study provides deep understanding on the amount and nature of deviation introduced by errors and how these errors affect estimators of parameters of a population with linear trend. Consideration is given to measurement errors that assume a normal distribution. Systematic sampling technique is used where a sample of size n is selected randomly from a finite population with a fixed interval a. Systematic sampling is considered instead of simple random sampling in this case because of its effectiveness in dealing with linear trend. The explicit values of population totals, means and variances together with their estimates are derived. The results indicate that there can be overestimate of the population mean if the expected systematic errors tend towards positive values and underestimate if the expected systematic error tend towards negative values. When random errors are considered, there is no effect on estimated population parameters.
Abstract: The study involves investigating the impact of measurement errors on estimators of parameters of a finite population with linear trend among population values, under systematic sampling. The study provides deep understanding on the amount and nature of deviation introduced by errors and how these errors affect estimators of parameters of a populati...
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Forecast Comparison of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Self Exciting Threshold Autoregressive (SETAR) Models
Akintunde Mutairu Oyewale,
Olalude Gbenga Adelekan,
Oseghale Osezuwa Innocient
Issue:
Volume 6, Issue 6, November 2017
Pages:
278-283
Received:
13 August 2017
Accepted:
31 August 2017
Published:
21 November 2017
Abstract: Financial and Economic time series literatures have shown that financial and economic time series data exhibit non-linearity in their behavior. In order to be mindful of such behavior as applied to Nigeria inflation rates, this study therefore, applies a two stages non-linear self-exciting threshold autoregressive model (SETAR) to Nigeria inflation rates. The results obtained for both in-sample and out-of-sample forecast performances for SETAR model were compared with results of linear seasonal autoregressive integrated moving average (SARIMA). On the basis of in-sample forecast performance of linear SARIMA and non-linear SETAR, using performance measure indices like MAE and RMSE, the results obtained indicated that non-linear SETAR model performed better than linear SARIMA. So also for the out-of-sample forecast performance using multi-step ahead forecast performance, the results also indicated that non-linear SETAR out performed linear SARIMA.
Abstract: Financial and Economic time series literatures have shown that financial and economic time series data exhibit non-linearity in their behavior. In order to be mindful of such behavior as applied to Nigeria inflation rates, this study therefore, applies a two stages non-linear self-exciting threshold autoregressive model (SETAR) to Nigeria inflation...
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Estimating Survival Probability of Drug Users with Application to Drug and Substance Abuse in Kenya
Robert Kasisi,
Joseph Koske,
Mathew Kosgei
Issue:
Volume 6, Issue 6, November 2017
Pages:
284-289
Received:
28 June 2017
Accepted:
17 July 2017
Published:
27 November 2017
Abstract: The contemporary studies on drug abuse have blamed the increasing menace of drug abuse on failure of governments to enact adequate laws prohibiting drug abuse and failure to place strict border controls to prevent entry of drugs. Others have blamed social media and modernization as key players towards the current trends of drug abuse. As a result studies have shifted from studying factors leading to drug abuse as these seem to be obvious to studying covariates that leading to improved probabilities of recovery upon treatment. Female substance users are said to be proportionately more likely to recover from drug use than male substance abusers. However studies have showed that female drug users experience low turnout for treatment from drug abuse. With the increasing trend of women drug users seeking treatment there is an urgent need to estimate survival probability of drug use subjects based on marital status, age, gender and job status. This study sought to determine the survival probability of drug users in Kenya for the period between July 2013 and June 2015. Kaplan Meier analysis was used to determine the survival probability of a subject entering into drug use at different stages of life based on predictive covariates. Survival probability of drug users based on age, gender, marital status and employment status was determined. The study recommended that there significant differences in survival probability based on gender, age, marital status and employment status. Therefore the study recommended that treatment services be tailored on treating subjects based on these predictive covariates.
Abstract: The contemporary studies on drug abuse have blamed the increasing menace of drug abuse on failure of governments to enact adequate laws prohibiting drug abuse and failure to place strict border controls to prevent entry of drugs. Others have blamed social media and modernization as key players towards the current trends of drug abuse. As a result s...
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Mixed Hidden Markov Models for Clinical Research with Discrete Repeated Measurements
Yosuke Inaba,
Asanao Shimokawa,
Etsuo Miyaoka
Issue:
Volume 6, Issue 6, November 2017
Pages:
290-296
Received:
4 October 2017
Accepted:
28 October 2017
Published:
7 December 2017
Abstract: A hidden Markov model (HMM) is a method for analyzing a sequence of transitions for a set of data by considering the outcomes Y to be output from latent state X, which has the Markov property. The HMM has been widely applied, with applications that include speech recognition, genomic analysis, and finance forecasting. The HMM was originally a method for dealing with single-process data. Thus, it is a natural extension to apply it to data with a repeated measure structure by incorporating random effects in it. This is called the mixed hidden Markov model (MHMM). With this extension, the MHMM was recently applied to clinical research data with repeated measurements, e.g. multiple sclerosis, alcohol consumption, and primary biliary cirrhosis. In relation to parameter inference, because regular HMM methods can be used in an MHMM framework, some legacy knowledge is applicable. The likelihood can be obtained by simply adding a random effect parameter to a single process HMM, and the conventional maximum-likelihood method can be used for parameter estimation. On the other hand, much work must still be performed. For instance, the mathematical property of the maximum likelihood estimator has not yet been thoroughly examined. In this study, the asymptotic normality and consistency of the maximum likelihood estimator of the MHMM concerned with time points are examined via simulation, and found that these properties were almost fine. These methods are applied to actual study data, and future perspectives are provided in the conclusion.
Abstract: A hidden Markov model (HMM) is a method for analyzing a sequence of transitions for a set of data by considering the outcomes Y to be output from latent state X, which has the Markov property. The HMM has been widely applied, with applications that include speech recognition, genomic analysis, and finance forecasting. The HMM was originally a metho...
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Construction of Second Order Rotatable Simplex Designs
Otieno-Roche Emily,
Koske Joseph,
Mutiso John
Issue:
Volume 6, Issue 6, November 2017
Pages:
297-302
Received:
2 June 2017
Accepted:
16 June 2017
Published:
7 December 2017
Abstract: Rotatable designs are mainly for the exploration of response surfaces. These designs provide the preferred property of constant prediction variance at all points that are equidistant from the design center, thus improving the quality of the prediction. Initially, they were constructed through geometrical configurations and several second order designs were obtained. Full Factorial Design of Experiment provides the most response information about factor main effects and interactions, the process model’s coefficients for all factors and interactions, and when validated, allows process to be optimized. On the other hand, mixture designs are a special case of response surface designs where prediction and optimization are the main goals. These designs usually predict all possible formulations of the ingredients however, little or no research has been done incorporating rotatability with the mixture designs. This paper therefore aims at constructing Rotatable Simplex Designs (RSDs) using the properties of Simplex - Lattice Designs (SLDs) in connection with Full Factorial Designs (FFDs).
Abstract: Rotatable designs are mainly for the exploration of response surfaces. These designs provide the preferred property of constant prediction variance at all points that are equidistant from the design center, thus improving the quality of the prediction. Initially, they were constructed through geometrical configurations and several second order desi...
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Construction of Weighted Second Order Rotatable Simplex Designs (Wrsd)
Otieno-Roche Emily,
Koske Joseph,
Mutiso John
Issue:
Volume 6, Issue 6, November 2017
Pages:
303-310
Received:
2 June 2017
Accepted:
16 June 2017
Published:
7 December 2017
Abstract: Response surface methodology is widely used for developing, improving, and optimizing processes in various fields. A rotatable simplex design is one of the new designs that have been suggested for fitting second-order response surface models. In this article, we present a method for constructing weighted second order rotatable simplex designs (WRSD) which are more efficient than the ordinary rotatable simplex designs (RSD). Using moment matrices based on the Simplex and Factorial Designs, and the General Equivalence Theorem (GET) for D- and A- optimality, weighted rotatable simplex designs (WRSDs) were obtained. A- and D- optimality criterion was then used to establish the efficiency of the designs.
Abstract: Response surface methodology is widely used for developing, improving, and optimizing processes in various fields. A rotatable simplex design is one of the new designs that have been suggested for fitting second-order response surface models. In this article, we present a method for constructing weighted second order rotatable simplex designs (WRSD...
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The Effects of Macroeconomic Indicators on Economic Growth of Nigeria (1970-2015)
Mustapha Abiodun Okunnu,
Matthew Iwada Ekum,
Oluwaseun Raphael Aderele
Issue:
Volume 6, Issue 6, November 2017
Pages:
325-334
Received:
21 March 2017
Accepted:
10 April 2017
Published:
24 December 2017
Abstract: According to World Bank statistics reported in 2016, the Gross Domestic Product per capita (RGDP) in Nigeria was 2,548.20 United States Dollar (USD) in 2015. Also, Nigeria’s highest ever GDP recorded was 568.508 billion USD in 2014, with this exceptional growth in the economy, the World Bank and National Bureau of Statistics (NBS) and Central Bank of Nigeria (CBN) forecast GDP for 2016 to be very high but unfortunately, the GDP published by NBS and CBN as at June 2016 was 22.26 billion USD, which was the lowest ever since returning to democracy in 1999. This sharp fall in GDP reduced Nigeria GDP per capita drastically. It is based on this we employed Dynamic Multiple Linear Regression Model to fit a model of RGDP of Nigeria using some world development indicators as explanatory variables. Data was collected from 1970 to 2015 from World Bank database and National Bureaus of Statistics (NBS) on the six World Development Indicators (WDI), total Import, official exchange rate, broad money, inflation rate, total natural resources rents and foreign direct investment. The dynamic weighted least square (DWLS) was used rather than the dynamic ordinary least square (DOLS). The result of the analysis shows that imports of goods and services positively affect RGDP of Nigeria significantly, while other explanatory variables negatively affect RGDP significantly. Based on this result, we recommend that rather than closing boarder to imports of goods and services, we need to restructure the economy, so that, Nigerian made goods can compete favorably with the imported goods and services, thereby reduce importation naturally instead of forcefully halt importation.
Abstract: According to World Bank statistics reported in 2016, the Gross Domestic Product per capita (RGDP) in Nigeria was 2,548.20 United States Dollar (USD) in 2015. Also, Nigeria’s highest ever GDP recorded was 568.508 billion USD in 2014, with this exceptional growth in the economy, the World Bank and National Bureau of Statistics (NBS) and Central Bank ...
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