In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariate...In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.展开更多
This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpol...This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpolation (OI), and 3D-Var. Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. The analysis package was applied to produce analyses at 6 h time interval for the period 1-11 August 2008. The period was selected for data availability and forty-one analyses were collected. The results show the validity of the different solutions, which can be chosen depending on the physical problem to solve and on the computational resources available. In particular, assuming the observations as the reference, all solutions show a decrease of the RMSE compared to the background. The decrease is consistent with the particular setting of the analysis system used in this paper. The comparison between different solutions shows that the SA converges to OI in few iterations, and that the SA solution with ten iteration is, in practice, equal to OI. Moreover, the 3D-Var method shows its potential to improve the analysis, once the horizontal and vertical length-scales and the background and observational errors are set optimally, because its solution may be sizeably different from two-dimensional methods.展开更多
文摘In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.
文摘This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpolation (OI), and 3D-Var. Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. The analysis package was applied to produce analyses at 6 h time interval for the period 1-11 August 2008. The period was selected for data availability and forty-one analyses were collected. The results show the validity of the different solutions, which can be chosen depending on the physical problem to solve and on the computational resources available. In particular, assuming the observations as the reference, all solutions show a decrease of the RMSE compared to the background. The decrease is consistent with the particular setting of the analysis system used in this paper. The comparison between different solutions shows that the SA converges to OI in few iterations, and that the SA solution with ten iteration is, in practice, equal to OI. Moreover, the 3D-Var method shows its potential to improve the analysis, once the horizontal and vertical length-scales and the background and observational errors are set optimally, because its solution may be sizeably different from two-dimensional methods.
