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Statistical Analysis of Determinants of Maternal Institutional Delivery Service Utilization in Ethiopia
Issue:
Volume 4, Issue 3, May 2015
Pages:
71-77
Received:
5 February 2015
Accepted:
9 March 2015
Published:
24 March 2015
Abstract: Utilization of maternal healthcare service is a proximate determinant of maternal morbidities and mortalities. On the way to improve maternal health care service, it is important to understand factors influencing maternal health care service utilization. The main purpose of this study is to determine statistically the correlates of maternal place of delivery in Ethiopia, using 2011 EDHS data. The overall frequency of maternal delivery at heath facility in Ethiopia was 12.8%. Logistic regression model was used to model the effects of selected socioeconomic and demographic covariates. It was found that the covariates place of residence, birth order of child, mother’s age at child birth, mother’s educational level, household economic status and mothers employment status were the most important determinants of mothers place of delivery in the Ethiopia. It is suggested that to improve maternal healthcare service utilization, maternal education should be supported as a policy and this could be achieved through female literacy programs in the country.
Abstract: Utilization of maternal healthcare service is a proximate determinant of maternal morbidities and mortalities. On the way to improve maternal health care service, it is important to understand factors influencing maternal health care service utilization. The main purpose of this study is to determine statistically the correlates of maternal place o...
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Robust Linear Regression Using L1-Penalized MM-Estimation for High Dimensional Data
Kamal Darwish,
Ali Hakan Buyuklu
Issue:
Volume 4, Issue 3, May 2015
Pages:
78-84
Received:
10 March 2015
Accepted:
24 March 2015
Published:
30 March 2015
Abstract: Large datasets, where the number of predictors p is larger than the sample sizes n, have become very popular in recent years. These datasets pose great challenges for building a linear good prediction model. In addition, when dataset contains a fraction of outliers and other contaminations, linear regression becomes a difficult problem. Therefore, we need methods that are sparse and robust at the same time. In this paper, we implemented the approach of MM estimation and proposed L1-Penalized MM-estimation (MM-Lasso). Our proposed estimator combining sparse LTS sparse estimator to penalized M-estimators to get sparse model estimation with high breakdown value and good prediction. We implemented MM-Lasso by using C programming language. Simulation study demonstrates the favorable prediction performance of MM-Lasso.
Abstract: Large datasets, where the number of predictors p is larger than the sample sizes n, have become very popular in recent years. These datasets pose great challenges for building a linear good prediction model. In addition, when dataset contains a fraction of outliers and other contaminations, linear regression becomes a difficult problem. Therefore, ...
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Comparative Study of Efficiency of Integer Programming, Simplex Method and Transportation Method in Linear Programming Problem (LPP)
Ayansola Olufemi Aderemi,
Oyenuga Iyabode Favour,
Abimbola Latifat Adebisi
Issue:
Volume 4, Issue 3, May 2015
Pages:
85-88
Received:
8 March 2015
Accepted:
24 March 2015
Published:
31 March 2015
Abstract: In this paper, we present a Linear Programming Problem (LPP) to minimize the cost of transportation of NBC, PLC products from three distribution centres to ten depots. Three methods of analysis were considered namely: Integer Programming, simplex method and transportation method via computer packages. The result of the analysis revealed that, the cost of transportation from these distribution centres to all the 10 depots are the same. That is, the optimal cost is N9, 127, 776.
Abstract: In this paper, we present a Linear Programming Problem (LPP) to minimize the cost of transportation of NBC, PLC products from three distribution centres to ten depots. Three methods of analysis were considered namely: Integer Programming, simplex method and transportation method via computer packages. The result of the analysis revealed that, the c...
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Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model
Samwel N. Mwenda,
Anthony Kibira Wanjoya,
Anthony Gichuhi Waititu
Issue:
Volume 4, Issue 3, May 2015
Pages:
89-98
Received:
17 February 2015
Accepted:
27 March 2015
Published:
3 April 2015
Abstract: Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 people in the sample was dropped because information about the individuals smoking pattern, age or work status could not be found. Data Analysis: The data was analysed using R-software version 3.0.2, and report presented in form of tables and graphs. Results: This project found out that there is likelihood of a person being a heavy smoker, light smoker or Non-smoker, if the person works in the Government and Non-government /private organization, self-employed or Unemployed. The overall effect of work status was statistically significant with a chi-square value of 129.722 (p-value<0.0001). Conclusion: The results show that a person’s working status and their age are good predictors of a specific smoking pattern. From the results we have more people smoking as they grow old.
Abstract: Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 ...
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Application of a Bivariate Poisson Model in Devising a Profitable Betting Strategy of the Zimbabwe Premier Soccer League Match Results
Desmond Mwembe,
Lizwe Sibanda,
Ndava Constantine Mupondo
Issue:
Volume 4, Issue 3, May 2015
Pages:
99-111
Received:
10 March 2015
Accepted:
27 March 2015
Published:
7 April 2015
Abstract: The study seeks to construct a profitable betting strategy for soccer results by developing a bivariate Poisson model for the analysis and computation of probabilities for football match outcomes. The dependence coefficient is estimated from Monte Carlo simulation and the scoring intensities are estimated from a log-linear model. The hypothesis tests show that the home-ground effect exists for some, but not all teams in the Zimbabwe Premier Soccer League. The profitable betting rule is to place a bet on the outcome of a particular match when a model's probabilistic forecast suggests a sufficient edge over the bookmaker's implied probability.
Abstract: The study seeks to construct a profitable betting strategy for soccer results by developing a bivariate Poisson model for the analysis and computation of probabilities for football match outcomes. The dependence coefficient is estimated from Monte Carlo simulation and the scoring intensities are estimated from a log-linear model. The hypothesis tes...
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Statistical Trend Analysis of Residential Water Demand in Kisumu City, Kenya
Robert Nyamao Nyabwanga,
Edgar Ouko Otumba,
Fredrick Onyango,
Simeyo Otieno
Issue:
Volume 4, Issue 3, May 2015
Pages:
112-117
Received:
30 March 2015
Accepted:
9 April 2015
Published:
18 April 2015
Abstract: This study sought to analyse trend in the monthly water demand data series in Kisumu city at both seasonal and non-seasonal levels using the parametric method of Ordinary Least Squares (OLS) and non-parametric methods of Mann-Kendall tau and Sen's T test. Sen’s test was applied to validate the Mann Kendall trend test and to estimate the magnitude of the trend and its direction. The significance of the slope of the OLS equation was tested using the F-Test based on the Analysis of Variance (ANOVA). Secondary monthly water consumption data obtained from KIWASCO for the period January 2004 to December 2013 were used. Using logarithmically transformed data, the study established by OLS that residential water demand in Kisumu City had a significant increasing trend (FCalc=(105.13) > F(1;119)(α=0:05)=(5.15)). Kendall's tau test corroborated the OLS results of a significant increasing trend. The Sens T test indicated that most of the months registered significant upward trend with Sen’s slope estimates showing positive rates of change in residential water demand for these months.
Abstract: This study sought to analyse trend in the monthly water demand data series in Kisumu city at both seasonal and non-seasonal levels using the parametric method of Ordinary Least Squares (OLS) and non-parametric methods of Mann-Kendall tau and Sen's T test. Sen’s test was applied to validate the Mann Kendall trend test and to estimate the magnitude ...
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Entropy for Past Residual Life Time Distributions
Arif Habib,
Meshiel Alalyani
Issue:
Volume 4, Issue 3, May 2015
Pages:
118-124
Received:
27 February 2015
Accepted:
16 March 2015
Published:
21 April 2015
Abstract: As we are familiar that existence of life is uncertain. In the context of reliability and lifetime distributions, there are some measures such as the hazard rate function or the mean residual lifetime function that have been used to characterize or compare the aging process of a component. This definition deals with random variable truncated above some t, i.e. the support of the random variable is taken to be (0, t). We outline some common methods for past residual lifetime distributions with the aim to provide some insights on general construction mechanisms. Some applications are given to provide the readers a possible source of ideas to draw upon. Applications of past residual lifetime distributions in reliability, survival analysis and mortality studies are briefly discussed.
Abstract: As we are familiar that existence of life is uncertain. In the context of reliability and lifetime distributions, there are some measures such as the hazard rate function or the mean residual lifetime function that have been used to characterize or compare the aging process of a component. This definition deals with random variable truncated above ...
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Cross-Country Spillovers in East Africa: A Global Vector Autoregressive Analysis
Daniel Njoora,
Olusanya E. Olubusoye,
Patrick Weke
Issue:
Volume 4, Issue 3, May 2015
Pages:
125-137
Received:
20 March 2015
Accepted:
11 April 2015
Published:
21 April 2015
Abstract: The recent financial crisis raises important issues about transmission of financial shocks across borders. This paper uses the global vector autoregressive model as developed in Dees, di Mauro, Pesaran and Smith (2007) to study cross-country interlinkages among East African countries. The paper uses trade weights to capture the importance of the foreign variables. Results reveal that there is no evidence of strong international linkages across countries in East Africa. Results also reveal that the variable in which a shock is simulated is the main channel through which-in the shortrun-shocks are transmitted, while the contribution of other variables becomes more important over longer horizons.
Abstract: The recent financial crisis raises important issues about transmission of financial shocks across borders. This paper uses the global vector autoregressive model as developed in Dees, di Mauro, Pesaran and Smith (2007) to study cross-country interlinkages among East African countries. The paper uses trade weights to capture the importance of the fo...
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Analysis of Mean Absolute Deviation for Randomized Block Design under Laplace Distribution
Issue:
Volume 4, Issue 3, May 2015
Pages:
138-149
Received:
2 April 2015
Accepted:
11 April 2015
Published:
21 April 2015
Abstract: Analysis of mean absolute deviation (ANOMAD) for randomized block design is derived where the total sum of absolute deviation (TSA) is partition into exact block sum of absolute deviation (BLSA), exact treatment sum of absolute deviation (TRSA) and within sum of absolute deviation (WSA). The exact partitions are derived by getting rid of the absolute function from MAD by using the idea of re-expressing the mean absolute deviation as a weighted average of data with sum of weights zero. ANOMAD has advantages: offers meaningful measure of dispersion, does not square data, and can be extended to other location measures such as median. Two ANOMAD graphs are proposed. However, the variance-gamma distribution is used to fit the sampling distributions for the mean of BLSA and the mean of TRSA. Consequently, two tests of equal means and medians are proposed under the assumption of Laplace distribution.
Abstract: Analysis of mean absolute deviation (ANOMAD) for randomized block design is derived where the total sum of absolute deviation (TSA) is partition into exact block sum of absolute deviation (BLSA), exact treatment sum of absolute deviation (TRSA) and within sum of absolute deviation (WSA). The exact partitions are derived by getting rid of the absolu...
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An Alternative Method of Estimation of SUR Model
Shohel Rana,
Mohammad Mastak Al Amin
Issue:
Volume 4, Issue 3, May 2015
Pages:
150-155
Received:
4 April 2015
Accepted:
14 April 2015
Published:
24 April 2015
Abstract: This paper proposed a transformed method of SUR model which provided unbiased estimation in case of two and three equations of high and low co-linearity for both small and large datasets. The generalized least squares (GLS) method for estimation of seemingly unrelated regression (SUR) model proposed by Zellner (1962), Srivastava and Giles (1987),provided higher MSE. Although the Ridge estimators proposed by Alkhamisi and Shukur (2008) provided smaller MSE in comparison with others, it was not unbiased in case of severe multicollinearity.This study showed that our proposed method typically provided unbiasedestimator with lower MSE and TMSE than traditional methods.
Abstract: This paper proposed a transformed method of SUR model which provided unbiased estimation in case of two and three equations of high and low co-linearity for both small and large datasets. The generalized least squares (GLS) method for estimation of seemingly unrelated regression (SUR) model proposed by Zellner (1962), Srivastava and Giles (1987),pr...
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Application of Logistic Regression in Determining the Factors Influencing the use of Modern Contraceptive among married women in Ethiopia
Issue:
Volume 4, Issue 3, May 2015
Pages:
156-162
Received:
20 March 2015
Accepted:
1 April 2015
Published:
5 May 2015
Abstract: The aim of the study was to investigate the determinants of use of modern contraceptive among married women in Ethiopia. Our study is based on the data taken from a nationally representative survey EDHS of 2011. The sample includes 9,438 married women aged 15-49 years. Cross tabulations were carried out at the bivariate level to assess the association between contraceptive use and each of the explanatory variables and binary multiple logistic regression analysis was used to identify the factors influencing modem contraceptive use among married women in Ethiopia. The bivariate analysis reveals statistically significant association between all explanatory variables i.e age of woman, region, religion, place of residence, education level of woman, number of living children, desire for more children, wealth status, and decision maker for modern contraception, educational level of husband, modern contraceptive knowledge and exposure to media. Results for binary multiple logistic regression analysis reveals that age of woman have a statistically significant positive effect on modern contraceptive use. Contraceptive use was highest in the age group of 15 to 19 years while it was lowest among married women aged 40-44 years compared to those married women aged 45-49 years as reference category. Furthermore, uneducated women and women not at work want no more children. The lowest wealth status women are less likely to use modern contraception compared to their corresponding reference group. The result also shows that married women who do not discuss about family planning with their husbands use modern contraception 25.6% less in comparison to those couples made decisions jointly. Generally men play a critical role in determining the size of their family. Male involvement, therefore, is an integral component of successful reproductive health programs. But binary logistic regression results do not support the hypothesis that educational levels of husband have influence on the use of modem contraceptive methods among women. Media exposure is another factor that influences modem contraceptive use. The odds of married women who were not exposed to media are 35.8 % less likely in using a modern contraception method than those who had media exposure.
Abstract: The aim of the study was to investigate the determinants of use of modern contraceptive among married women in Ethiopia. Our study is based on the data taken from a nationally representative survey EDHS of 2011. The sample includes 9,438 married women aged 15-49 years. Cross tabulations were carried out at the bivariate level to assess the associat...
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Mathematical Modeling of Optimum 3 Step Stress Accelerated Life Testing for Generalized Pareto Distribution
Sadia Anwar,
Sana Shahab,
Arif Ul Islam
Issue:
Volume 4, Issue 3, May 2015
Pages:
163-169
Received:
17 April 2015
Accepted:
28 April 2015
Published:
11 May 2015
Abstract: This article contains the optimum 3 step stress accelerated life test under cumulative exposure model. The lifetimes of test units are assumed to follow a generalized Pareto distribution. The scale parameter of the used failure time distribution at the constant stress level is assumed to have a log-linear and quadratic relationship with the stress. A comparison between linear plan and quadratic plan by maximum likelihood estimators for the different sample sizes is shown in the table. The optimum test plans is obtained by minimizing the asymptotic variance of the maximum likelihood estimator of the percentile of the lifetime distribution at normal stress condition for the model parameters. Tables of optimum times of changing stress level for both plans are also obtained.
Abstract: This article contains the optimum 3 step stress accelerated life test under cumulative exposure model. The lifetimes of test units are assumed to follow a generalized Pareto distribution. The scale parameter of the used failure time distribution at the constant stress level is assumed to have a log-linear and quadratic relationship with the stress....
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Multinomial Logit Modeling of Factors Associated With Multiple Sexual Partners from the Kenya Aids Indicator Survey 2007
Beryl Ang’iro,
Samuel Mwalili,
Josphat Kinyanjui
Issue:
Volume 4, Issue 3, May 2015
Pages:
170-177
Received:
4 March 2015
Accepted:
19 March 2015
Published:
16 May 2015
Abstract: The number of lifetime sex partners of an individual has an important effect on Human Immunodeficiency Virus (HIV) status of an individual; hence modeling multiple sexual partnerships is an essential component of any analysis of HIV outcome. Multiple sexual partnerships are associated with greater risk of HIV, Sexually Transmitted infections (STIs) and intimate partner violence. This research project presents a general approach for modeling logit of clustered (correlated) ordinal and nominal responses using polytomous data from the Kenya AIDS Indicator Survey 2007 (NASCOP 2010). We review multinomial logit models as generalized linear models. The model is applied to HIV prevalence data among men and women in Kenya, derived from the Kenya AIDS Indicator Survey 2007 (KAIS). We generalize logistic regression to handle multinomial response variables, with separate models for nominal and ordinal cases. When modeling a nominal response variable we are interested in finding if certain predictors have an effect on the probabilities. The baseline category logit model, models the odds of being in one category relative to being in a designated category (last category), for all pairs of categories. It is used for nominal responses. A maximum likelihood estimation (MLE) approach is used for baseline category logit model. To model an ordinal response variable one models the cumulative response probabilities or cumulative odds. The cumulative logit model is used when the response of an individual unit is restricted to one of a finite number of ordinal values. This study shows the practicality of multinomial logit model in analyzing epidemiological data. Other studies have found education to be associated with multiple sexual partners. In this study, we observed that multiple sexual partners is not related to education. Other covariates like Gender, Place of residence, sexually active individuals for the past 12 months and marital status were found to be associated with multiple sexual partners. Individuals that are sexually active for the past 12 months were found to be ten times more likely to have multiple sexual partners compared to those that are not. After controlling for all other factors, the odds of male to female having multiple sexual partners doubled to 7.6 meaning male are almost 8 times likely to have multiple sexual partners compared to female. Partner testing or couples testing is a main strategy of national testing initiatives in Kenya. Respondents are encouraged to learn their test results with their partner.
Abstract: The number of lifetime sex partners of an individual has an important effect on Human Immunodeficiency Virus (HIV) status of an individual; hence modeling multiple sexual partnerships is an essential component of any analysis of HIV outcome. Multiple sexual partnerships are associated with greater risk of HIV, Sexually Transmitted infections (STIs)...
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Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach
Julius Nyerere Odhiambo,
Anthony Kibira Wanjoya,
Anthony Gichuhi Waititu
Issue:
Volume 4, Issue 3, May 2015
Pages:
178-184
Received:
5 May 2015
Accepted:
13 May 2015
Published:
27 May 2015
Abstract: Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.
Abstract: Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and pre...
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Modeling Panel Data: Comparison of GLS Estimation and Robust Covariance Matrix Estimation
Victor Muthama Musau,
Anthony Gichuhi Waititu,
Anthony Kibira Wanjoya
Issue:
Volume 4, Issue 3, May 2015
Pages:
185-191
Received:
8 May 2015
Accepted:
17 May 2015
Published:
28 May 2015
Abstract: The proliferation of panel studies which has been greatly motivated by the availability of data and greater capacity for modeling the complexity of human behavior than a single cross-section or time series data has led to the rise of challenging methodologies for estimating the data sets. Much controversy on these methodologies is the under-estimation of the standard errors leading to wrong conclusions of the involved hypothesis test as well as making inappropriate inference to the underlying model parameters. This is due to the heteroscedasticity and autocorrelation nature of the disturbance term in the classical linear regression model. This study sought to estimate linear-panel model parameters using conventional regression techniques which have the capacity to address the correlation and heteroscedasticity problem. By relaxing the homogeneity and non-correlation properties of the disturbance term in the classical linear regression model, we employed the generalized least squares method to estimate the model parameters. Using the available White Heteroscedasticity Consistent estimators i.e. HC0, HC1, HC2, HC3 and HC4, we also obtained estimates for the generalized ordinary least squares covariance matrix.
Abstract: The proliferation of panel studies which has been greatly motivated by the availability of data and greater capacity for modeling the complexity of human behavior than a single cross-section or time series data has led to the rise of challenging methodologies for estimating the data sets. Much controversy on these methodologies is the under-estimat...
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Comparison of Methods of Handling Missing Data: A Case Study of KDHS 2010 Data
Shelmith Nyagathiri Kariuki,
Anthony Waititu Gichuhi,
Anthony Kibira Wanjoya
Issue:
Volume 4, Issue 3, May 2015
Pages:
192-200
Received:
8 May 2015
Accepted:
18 May 2015
Published:
29 May 2015
Abstract: Missing data poses a major threat to observational and experimental studies. Analysis of data having ignored missingness results to estimates that are inefficient and unbiased. Various researches have been done to determine the best methods of dealing with missing data. The analysis used in these researches involved simulating missing data from complete data. Missing data are then imputed using the various methods, and the best method is arrived at by looking at the biasness of the imputed estimates, from the complete data estimates and the magnitude of standard errors. This study aimed at establishing the best method of dealing with missing data, based on the goodness of fit tests. The study made use of data from KDHS 2010. The overall rate of missingness was about 80%. The missing data mechanism was tested and proved to be MAR. The missing data was then imputed using Expectation Maximization Algorithm and Multiple Imputation. Later, logistic models were fitted to both datasets. Afterwards, goodness of fit tests were carried out to determine which of the two methods was the better method for imputing data. These tests were the AIC, Root Mean Square Error of Approximation (RMSEA) and Cox and Snell’s R-Squared. The predictive ability of the two models was also examined using confusion matrices and the area under receiver operation curve (AUROC). From these tests, multiple imputation was seen to be the better method of imputation since logistic regression model fitted the data better as compared to data imputed using expectation maximization. From the results of the study, the researchers recommend that the type of missingness present in data should be examined. If the amount of missing data is large, and the data is MAR, then data should be imputed using multiple imputation before any inference are made. The researchers suggested more research to be done to determine the maximum rate of missing data that should be imputed.
Abstract: Missing data poses a major threat to observational and experimental studies. Analysis of data having ignored missingness results to estimates that are inefficient and unbiased. Various researches have been done to determine the best methods of dealing with missing data. The analysis used in these researches involved simulating missing data from com...
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A Design Unbiased Variance Estimator of the Systematic Sample Means
Festus A. Were,
George Orwa,
Romanus Odhiambo
Issue:
Volume 4, Issue 3, May 2015
Pages:
201-210
Received:
11 May 2015
Accepted:
18 May 2015
Published:
30 May 2015
Abstract: Systematic sampling is normally used in surveys of finite populations because of its appealing simplicity and efficiency. When properly applied, it can reflect stratification in the population and thus can be more precise than SRS. In systematic sampling technique, the sampling units are evenly spread over the whole population. This sampling scheme is very sensitive to correlation between units in the entire population. A positive autocorrelation reduces the precision while a negative autocorrelation will improve the precision compared to simple random sampling. The limitation of this sampling method is that, it is not possible to estimate the design variance that is unbiased. This study proposes an estimator for the design variance based on a non-parametric model for the population using local polynomial regression as the estimation technique. The non-parametric model is more flexible that it can hold for many practical situations. A simulation study is performed to enable the comparison of the efficiency of the proposed estimator to the existing ones. The performance measures used include: Relative Bias (RB) and Mean Square Error (MSE). From the simulation results, it can be seen that local polynomial estimator based on nonparametric model is consistent and design unbiased for the variance of systematic sample mean. The simulation study gave smaller values for the relative biases and mean squared errors for proposed estimator.
Abstract: Systematic sampling is normally used in surveys of finite populations because of its appealing simplicity and efficiency. When properly applied, it can reflect stratification in the population and thus can be more precise than SRS. In systematic sampling technique, the sampling units are evenly spread over the whole population. This sampling scheme...
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Research on Beijing Total Logistics Demand Prediction Based on Grey Prediction Model
Jie Zhu,
Hong Zhang,
Li Zhou
Issue:
Volume 4, Issue 3, May 2015
Pages:
211-222
Received:
23 April 2015
Accepted:
6 May 2015
Published:
1 June 2015
Abstract: As an important part of regional logistics demand forecasting in logistics system planning, in order to develop regional logistics development policy, planning and construction of logistics infrastructure, provide the basis for the necessary for analysis of the logistics market development trend. Due to the development of China's logistics industry is still in its infancy stage, various statistical data of logistics demand forecast needs is not complete, in the limited sample data, how to make a reasonable demand forecast for regional logistics has become a very important research topic. Beijing as Chinese political economic center, is the domestic cargo turnover, and an important hub for import and export, As the core of the Bohai Sea Economic Circle, Beijing in the regional economic development as a transport hub and logistics channel plays a more and more important role, therefore, should be on the Beijing logistics demand forecast analysis. This paper analyzes the present situation of Beijing logistics development, starting from the total economic output, economic structure, economic location and other aspects, basic economic situation of Beijing is analyzed. From the transportation infrastructure construction present situation, the current status of development of logistics industry, logistics enterprises are analyzed in terms of status and problems of Beijing logistics development; then further analysis of Beijing logistics development environment, all of these indicate that it is very necessary for Beijing logistics demand forecast. Using the econometric model to analyze and forecast the total demand analysis of Beijing logistics, discusses the influencing factors of Beijing logistics demand, thus the construction of index system of logistics demand forecast, and selects freight, freight turnover as a quantitative index to measure the total quantity of logistics demand, using the Eviews model and the analysis, obtains the Beijing logistics demand presents the fast growth to the situation in the future five years.
Abstract: As an important part of regional logistics demand forecasting in logistics system planning, in order to develop regional logistics development policy, planning and construction of logistics infrastructure, provide the basis for the necessary for analysis of the logistics market development trend. Due to the development of China's logistics industry...
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