Browsing คณะและวิทยาลัย by Author "Dryver, Arthur L, advisor"
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Double and resampling in adaptive cluster sampling Nipaporn Pochai; Dryver, Arthur L, advisor (National Institute of Development Administration, 2006)
Evaluating credit scoring models Vesarach Aumeboonsuke; Dryver, Arthur L, advisor (National Institute of Development Administration, 2011)
Evaluating the credit worthiness of credit seekers is a crucial process for financial institutions simply because their existence largely depends on how such a process is conducted. Financial institutions use a variety of credit scoring methods and a variety of criteria to select the best credit scoring methods. The primary purpose of this research is to evaluate the performance of some of the existing popular credit scoring methods that are widely used by financial institutions. The credit scoring methods to be considered for comparison purpose ...
Path sampling Mena Patummasut; Dryver, Arthur L, advisor (National Institute of Development Administration, 2011)
In sampling spatial populations, one part of the cost is due to the distance travelled to observe all of the units in a sample. Cluster sampling is one such sampling design which is often used specifically to address this issue. Even in cluster sampling, the researcher may have to travel great distances from the cluster to cluster selected. In an optimal setting, when sampling costs are mainly a function of distance travelled, researchers could sample all of the units in the path travelled during the sampling. For this reason, the authors are ...
Variance estimation for adaptive cluster sampling with a single primary unit and the partially systematic adaptive cluster sampling Urairat Netharn; Dryver, Arthur L, advisor (National Institute of Development Administration, 2009)
Two topics are investigated in this dissertation. The first concerns variance estimation when a single primary sampling unit is selected. Two new bias variance estimators, based on splitting the initial sample into sub-samples and regarding the initial sample as a stratified sample, are proposed. The results of this study indicated that both new variance estimators are underestimated. The first variance estimator is not preferable when the number of sub-samples is two because its relative bias is too large to be useful. Increasing the number of ...