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First–Passage Time Moment Approximation for the General Diffusion Process to a General Moving Barrier
Basel Mohammad Said Al-Eideh
Issue:
Volume 7, Issue 5, September 2018
Pages:
167-172
Received:
13 June 2018
Accepted:
9 July 2018
Published:
2 August 2018
Abstract: The problem of determining the first-passage times to a moving barrier for diffusion and other Markov processes arises in biological modeling, population growth, statistics, engineering, etc. Since the development of mathematical models for population growth of great importance in many fields. Therefore, the growth and decline of real populations can, in many cases, be well approximated by the solutions of stochastic differential equations. However, there are many solutions in which the essentially random nature of population growth should be taken into account. This paper focusses in approximating the moments of the first – passage time for the general diffusion process to a general moving barrier. This was done by approximating the differential equations by equivalent difference equations.
Abstract: The problem of determining the first-passage times to a moving barrier for diffusion and other Markov processes arises in biological modeling, population growth, statistics, engineering, etc. Since the development of mathematical models for population growth of great importance in many fields. Therefore, the growth and decline of real populations c...
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A Univariate Time Series Autoregressive Integrated Moving Average Model for the Exchange Rate Between Nigerian Naira and US Dollar
Nasiru Mukaila Olakorede,
Samuel Olayemi Olanrewaju,
Maji Yusufu Ugbede
Issue:
Volume 7, Issue 5, September 2018
Pages:
173-179
Received:
9 March 2018
Accepted:
30 March 2018
Published:
6 August 2018
Abstract: This research fit a univariate time series ARIMA model to the Monthly data of exchange rate between Nigerian Naira and US Dollar from January 1980 to December 2015. The Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model was estimated and the best fitted ARIMA model is used to obtain the post-sample forecasts for three years (January 2016 to December 2018). The data was analyzed with the aid of R statistical package and the best model was selected using Auto. ARIMA. The fitted model is ARIMA (0,1,1) with Akaike Information Criteria (AIC) of 2313.19, Normalized Bayesian Information Criteria (BIC) of 2325.39. This model was further validated by Ljung-Box test with no significant Autocorrelation between the residuals at different lag times and subsequently by white noise of residuals from the diagnostic check performed which clearly portray randomness of the standard error of the residuals, no significant spike in the residual plots of ACF and PACF. The forecasts value indicates clearly that Naira will continue to depreciate against the US Dollar between the periodsunderstudy.
Abstract: This research fit a univariate time series ARIMA model to the Monthly data of exchange rate between Nigerian Naira and US Dollar from January 1980 to December 2015. The Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model was estimated and the best fitted ARIMA model is used to obtain the post-sample forecasts for three years (January...
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Necessary Conditions for Isolation of Special Classes of Bilinear Autoregressive Moving Average Vector (BARMAV) Models
Anthony Effiong Usoro,
Eyo Awakessien Clement
Issue:
Volume 7, Issue 5, September 2018
Pages:
180-187
Received:
22 July 2018
Accepted:
7 August 2018
Published:
4 September 2018
Abstract: Bilinear Autoregressive Moving Average Vector (BARMAV) Models are models aggregated with the linear and non-linear vector components of autoregressive and moving average processes. The linear part is the sum of the two vector processes, while the non-linear part is the product of the processes. From the general BARMAV models, Bilinear Autoregressive Vector (BARV) Models and Bilinear Moving Average Vector (BMAV) Models have been isolated. Under certain conditions, the models are proved to exist. Empirically, Nigerian consumer price index and inflation rate are used to test the fitness of the bilinear models. Data for the analysis are from Central Bank of Nigeria Statistical Bulletin, collected from January 2009 to December 2016 with November 2009 as the base year for each of the series. The bilinear autoregressive moving average vector models are fitted to the data. Parameters are tested and found to be significant. The adequacy of each estimated model is confirmed with ACF, PACF and descriptive statistics adopted in the paper. The plots of the actual and fitted CPI and IR have shown that models are adequate as estimates compete favourably with the actual values. The models are useful in modelling some economic and financial data that exhibit some characteristics of non-linearity.
Abstract: Bilinear Autoregressive Moving Average Vector (BARMAV) Models are models aggregated with the linear and non-linear vector components of autoregressive and moving average processes. The linear part is the sum of the two vector processes, while the non-linear part is the product of the processes. From the general BARMAV models, Bilinear Autoregressiv...
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Statistical Analysis of Factors Affecting Poverty Status of Rural Residence
Bereket Getachew Mamo,
Markos Abiso
Issue:
Volume 7, Issue 5, September 2018
Pages:
188-192
Received:
5 July 2018
Accepted:
6 August 2018
Published:
21 September 2018
Abstract: Poverty is one of the serious problem affect the life of peoples in third world countries. Identifying major factors affecting poverty status of a society is important to decide what action should be taken to alleviate the poverty. The aim of this paper is to assess the factors that affect the poverty status of rural Residence in the study area. A cross-sectional study was conducted in five districts of Gamo Gofa zone, Southern Regional State of Ethiopia. From a total of households in these areas, 4092 were selected using stratified random sampling technique. Data were collected with a well designed questionnaire. If the welfare of a household is below the poverty line, the household is categorized as under poverty and if it is above poverty line, then the household is above poverty. Binary logistic regression model was used to analyze the data using the SPSS software. Several risk factors were found to be significant at the level of 5%. Saving culture, access to credit, resource base, land fertility, use of agricultural inputs, use of improved tools, availability of rain, land topography, labor availability and dependency attitude have significant association with the poverty status of a households. Governments and Non-Governmental organization should be aware of the consequences of these factors which can influence the household income and future poverty status.
Abstract: Poverty is one of the serious problem affect the life of peoples in third world countries. Identifying major factors affecting poverty status of a society is important to decide what action should be taken to alleviate the poverty. The aim of this paper is to assess the factors that affect the poverty status of rural Residence in the study area. A ...
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A Comparison of Poisson Model and Modified Poisson Model in Modelling Relative Risk of Childhood Diabetes in Kenya
Christine Gacheri Mutuura,
Anthony Kibira Wanjoya,
Isaiah Njoroge Mwangi
Issue:
Volume 7, Issue 5, September 2018
Pages:
193-199
Received:
26 May 2015
Accepted:
7 June 2015
Published:
11 October 2018
Abstract: This study models the relative risk of diabetes, taking obesity and malnutrition as the major risk factors to define exposure, using three different prevalence rates i.e. 3%, 7% and 11% (estimates and projections from various studies). Secondary data consisting of a sample population of 300 children from the Kenya Diabetes Management and Information Centre (DMI), a national central diabetes registry, databases is used. In this research project, the modified Poisson regression approach is used to directly estimate the relative risk of pediatric diabetes in age strata of patients aged between the ages of 0-14years inclusive and for the purpose of model comparison RR estimation is done using Poisson regression which will prove to be less desirable for assessment of risk in this study proving the modified Poisson model gives the best estimates. From the data used in this study it is evident that: exposure (being overweight or underweight) is not a risk factor for diabetes onset in children aged 0-14 years.
Abstract: This study models the relative risk of diabetes, taking obesity and malnutrition as the major risk factors to define exposure, using three different prevalence rates i.e. 3%, 7% and 11% (estimates and projections from various studies). Secondary data consisting of a sample population of 300 children from the Kenya Diabetes Management and Informatio...
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