Papers by Etebong Clement
A Statistical Time Series model is fitted to the Chemical Viscosity Reading data. Comparison with... more A Statistical Time Series model is fitted to the Chemical Viscosity Reading data. Comparison with the original models fitted to the same data set by Box and Jenkins is made using the Normalized Bayesian Information Criterion (BIC) and analysis and evaluation are presented. The analysis proved that the proposed model is superior to the Box and Jenkins models.
In sample surveys that incorporate auxiliary information, the precision of the survey estimates i... more In sample surveys that incorporate auxiliary information, the precision of the survey estimates is always improved when multiple auxiliary information are available. Calibration is used in survey sampling to include auxiliary information. In the presence of powerful auxiliary variables, the calibration estimation meets the objective of reducing both the non-response bias and the sampling error. In this paper, multivariate calibration estimator for domain totals in stratified random sampling design is proposed using multiple auxiliary variables. Analytical approach for obtaining optimum calibration weights is developed. The efficiency gain of the proposed calibration based approach estimator vis-à-vis conventional estimators is studied through simulation.
Calibration approach in survey sampling provides an important class of technique for the efficien... more Calibration approach in survey sampling provides an important class of technique for the efficient combination of data sources to improve the precision of parameter estimates. This paper introduces calibration approach separate ratio estimator for population mean í µí± ̅ of the study variable í µí±¦ using auxiliary variable í µí±¥ in stratified sampling. The variance and variance estimator of the proposed estimator have been derived using analytical approaches. An empirical study to evaluate the relative performances of the proposed estimator against members of its class was carried out. Results of analysis showed that the proposed estimator is substantially more efficient than members of its class under consideration with appreciable efficiency gain
This paper develops the theory of calibration estimation for ratio estimator and proposes calibra... more This paper develops the theory of calibration estimation for ratio estimator and proposes calibration approach alternative ratio estimator for population mean of the study variable using auxiliary variable in stratified sampling. The bias and variance of the proposed estimator have been derived under large sample approximation. Asymptotic optimum estimator and its approximate variance estimator are derived. An empirical study to evaluate the relative performances of the proposed estimator against members of its class is carried out.
A compositional time series is a multivariate time series in which each of the series has values ... more A compositional time series is a multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. The main approach to analyzing Compositional Time Series data has been based on the application of an initial transform to break the unit sum constraint. Box-Cox transformation originally was envisioned as a panacea for simultaneously correcting normality, linearity and homoscedasticity. However, one thing is clear; that seldom does this transformation fulfill the basic assumptions as originally suggested. This paper aims at reviewing works relating to these transformations with some modifications and illustrative example as would be applicable to the analysis of compositional time series data.
The Bayesian Approach is explored as an alternative to the Minimax Approach used in estimating pr... more The Bayesian Approach is explored as an alternative to the Minimax Approach used in estimating processes with continuous time parameter. The choice is obvious; Bayesian Approach is not only an alternative to the Minimax Solution. A Bayes estimator is always a function of the Minimal Sufficient Statistic, and under quite general conditions, the Bayes estimator corresponding to an arbitrary prior density function which is positive for all í µí¼í µí¼ í µí¼í µí¼ Θ is Consistent and Best Asymptotically Normal (BAN). A Bayes estimator with known prior distribution is optimum in the sense that it minimizes average loss. Its superlative quality of estimating both point and interval estimates proves its efficacy as an efficient statistical estimation criterion for continuous time parametric processes.
An analytical approach for finding the best sampling design subject to a cost constraint is devel... more An analytical approach for finding the best sampling design subject to a cost constraint is developed. We consider stratified random sampling design when elements of the inclusion probabilities are not equal but are in same stratum and proposed estimators of totals for domains of study under nonresponse in the context of calibration estimation. We derived optimum stratum sample sizes for a given set of unit costs for the sample design and compared empirically the relative performances of the proposed calibration estimators with a corresponding global estimator. Analysis and evaluation are presented.
The study aims at fitting a statistical time series model to the Forex rate data between the Nige... more The study aims at fitting a statistical time series model to the Forex rate data between the Nigeria naira and the U.S. dollar. The plots of the sample acf and pacf of the original series indicated that the series was not stationary. Transformation of the series was made by differencing to obtain stationarity. Following the distribution of the acf and pacf of the differenced series, an ARIMA (0, 1, 1) model was identified, the parameters of the model were estimated and diagnostically checked to prove its statistical significant and adequacy at both 0.05 and 0.01 –levels of significance under the Ljung-Box goodness of fit test. The Normalized Bayesian Information Criterion (BIC) was explored to confirm the adequacy of the model. Again, among a class of significantly adequate set of ARIMA (p,d,q) models of the same data set, the ARIMA (0,1,1) model was found as the most suitable model with least BIC value of –2.366, MAPE of 2.424, RMSE of 0.301 and R-square of 0.749. Estimation by Ljung-Box test with Q (18) = 9.746, 16 d.f and p-value of 0.880 showed no autocorrelation between residuals at different lag times. Finally, a forecast for a lead time () of 12 was made.
An analytical approach for finding the best sampling design subject to a cost constraint is devel... more An analytical approach for finding the best sampling design subject to a cost constraint is developed. We consider stratified random sampling design when elements of the inclusion probabilities are not equal but are in same stratum and proposed estimators of totals for domains of study under nonresponse in the context of calibration estimation. We derived optimum stratum sample sizes for a given set of unit costs for the sample design and compared empirically the relative performances of the proposed calibration estimators with a corresponding global estimator. Analysis and evaluation are presented.
The study aims at fitting a statistical time series model to the Forex rate data between the Nige... more The study aims at fitting a statistical time series model to the Forex rate data between the Nigeria naira and the U.S. dollar. The plots of the sample acf and pacf of the original series indicated that the series was not stationary. Transformation of the series was made by differencing to obtain stationarity. Following the distribution of the acf and pacf of the differenced series, an ARIMA (0, 1, 1) model was identified, the parameters of the model were estimated and diagnostically checked to prove its statistical significant and adequacy at both 0.05 and 0.01 –levels of significance under the Ljung-Box goodness of fit test. The Normalized Bayesian Information Criterion (BIC) was explored to confirm the adequacy of the model. Again, among a class of significantly adequate set of ARIMA (p,d,q) models of the same data set, the ARIMA (0,1,1) model was found as the most suitable model with least BIC value of –2.366, MAPE of 2.424, RMSE of 0.301 and R-square of 0.749. Estimation by Ljung-Box test with Q (18) = 9.746, 16 d.f and p-value of 0.880 showed no autocorrelation between residuals at different lag times. Finally, a forecast for a lead time () of 12 was made.
A Statistical Time Series model is fitted to the Chemical Viscosity Reading data. Comparison with... more A Statistical Time Series model is fitted to the Chemical Viscosity Reading data. Comparison with the original models fitted to the same data set by Box and Jenkins is made using the Normalized Bayesian Information Criterion (BIC) and analysis and evaluation are presented. The analysis proved that the proposed model is superior to the Box and Jenkins models.
Small Area Estimation is important in survey analysis when domain (subpopulation) sample sizes ar... more Small Area Estimation is important in survey analysis when domain (subpopulation) sample sizes are too small to provide adequate precision for direct domain estimators. Small Area Estimation (SAE) is a mathematical technique for extracting more detailed information from existing data sources by statistical modeling. The estimates are often mapped, so the technique is often generically called mapping. These maps and estimates (together with estimates of accuracy) are key information in aid allocation within a country. They are also increasingly important inputs to negotiations on allocation of international aid to particular countries. This paper provides a critical review of the main advances in small area estimation (SAE) methods in recent years with application to disease mapping. The review discusses in detail earlier developments of small area estimation methods in the field of disease mapping which serve as a necessary background for the new studies in disease mapping of small areas which we termed " Extensions ". Illustrative examples of the application of Small Area Estimation (SAE) to disease mapping are presented.
The phenomenon of nonresponse in a sample survey reduces the precision of parameters estimates an... more The phenomenon of nonresponse in a sample survey reduces the precision of parameters estimates and increases bias in estimates resulting in larger mean square error, thus ultimately reducing their efficiency. An important technique to address these problems is by calibration. We proposed calibration estimators for totals of domain of study. Sample designs and in particular sample sizes are chosen so as to provide reliable estimates for domains of study. But budget and other constraints usually prevent the allocation of sufficiently large samples to domains to provide reliable estimates using traditional statistical techniques. We have developed an approach for finding the best sample design for the domain calibration estimators subject to a cost constraint and derived optimum stratum sample sizes that minimized the variances of the proposed domain calibration estimators and reduced the objective function. The efficacy of the proposed domain calibration estimators was tested through a real data analysis. Results of the analytical study using real data showed that our proposed domain calibration estimator is substantially superior to the traditional GREG-estimator with relatively small bias, mean square error and average length of confidence interval.
In sample surveys that incorporate auxiliary information, the precision of the survey estimates i... more In sample surveys that incorporate auxiliary information, the precision of the survey estimates is always improved when multiple auxiliary information are available. Calibration is used in survey sampling to include auxiliary information. In the presence of powerful auxiliary variables, the calibration estimation meets the objective of reducing both the non-response bias and the sampling error. In this paper, multivariate calibration estimator for domain totals in stratified random sampling design is proposed using multiple auxiliary variables. Analytical approach for obtaining optimum calibration weights is developed. The efficiency gain of the proposed calibration based approach estimator vis-à-vis conventional estimators is studied through simulation.
Calibration approach in survey sampling provides an important class of technique for the efficien... more Calibration approach in survey sampling provides an important class of technique for the efficient combination of data sources to improve the precision of parameter estimates. This paper introduces calibration approach separate ratio estimator for population mean í µí± ̅ of the study variable í µí±¦ using auxiliary variable í µí±¥ in stratified sampling. The variance and variance estimator of the proposed estimator have been derived using analytical approaches. An empirical study to evaluate the relative performances of the proposed estimator against members of its class was carried out. Results of analysis showed that the proposed estimator is substantially more efficient than members of its class under consideration with appreciable efficiency gain
This paper develops the theory of calibration estimation for ratio estimator and proposes calibra... more This paper develops the theory of calibration estimation for ratio estimator and proposes calibration approach alternative ratio estimator for population mean of the study variable using auxiliary variable in stratified sampling. The bias and variance of the proposed estimator have been derived under large sample approximation. Asymptotic optimum estimator and its approximate variance estimator are derived. An empirical study to evaluate the relative performances of the proposed estimator against members of its class is carried out.
A compositional time series is a multivariate time series in which each of the series has values ... more A compositional time series is a multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. The main approach to analyzing Compositional Time Series data has been based on the application of an initial transform to break the unit sum constraint. Box-Cox transformation originally was envisioned as a panacea for simultaneously correcting normality, linearity and homoscedasticity. However, one thing is clear; that seldom does this transformation fulfill the basic assumptions as originally suggested. This paper aims at reviewing works relating to these transformations with some modifications and illustrative example as would be applicable to the analysis of compositional time series data.
The Bayesian Approach is explored as an alternative to the Minimax Approach used in estimating pr... more The Bayesian Approach is explored as an alternative to the Minimax Approach used in estimating processes with continuous time parameter. The choice is obvious; Bayesian Approach is not only an alternative to the Minimax Solution. A Bayes estimator is always a function of the Minimal Sufficient Statistic, and under quite general conditions, the Bayes estimator corresponding to an arbitrary prior density function which is positive for all í µí¼í µí¼ í µí¼í µí¼ Θ is Consistent and Best Asymptotically Normal (BAN). A Bayes estimator with known prior distribution is optimum in the sense that it minimizes average loss. Its superlative quality of estimating both point and interval estimates proves its efficacy as an efficient statistical estimation criterion for continuous time parametric processes.
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Papers by Etebong Clement