An effective and economic estimation of population mean in stratified random sampling using a linear cost function.

Improvement in the estimation of population mean has been an area of interest in sampling theory. So many estimators have been suggested for elevated estimation of the population mean in stratified random sampling, but there is still a gap for more closely estimating the population mean. In this paper, the authors propose a ratio-product-cum-exponential-cum-logarithmic type estimator for the enhanced estimation of population mean by implying one auxiliary variable in stratified random sampling using conventional ratio, exponential ratio, and logarithmic ratio type estimators. The suggested estimator is a generalization of ratio, exponential ratio, and logarithmic ratio type estimators, and therefore these are special cases of the proposed estimator. The proposed estimator's bias and MSE are determined and compared with those of influential estimators, with the linear cost function being used to investigate and compare alternatives. Use Cramer's rule to determine the optimal value of the proposed estimator. The proposed estimator is more effective than other existing estimators, according to theoretical observations. For various applications, we suggest using a proposed estimator with the minimal MSE, which is verified by a numerical example, to have practical applicability of theoretical conclusions in real life.

Respiratory cancer database: An open access database of respiratory cancer gene and miRNA.

AIMS: Respiratory cancer database (RespCanDB) is a genomic and proteomic database of cancer of respiratory organ. It also includes the information of medicinal plants used for the treatment of various respiratory cancers with structure of its active constituents as well as pharmacological and chemical information of drug associated with various respiratory cancers. MATERIALS AND METHODS: Data in RespCanDB has been manually collected from published research article and from other databases. Data has been integrated using MySQL an object-relational database management system. MySQL manages all data in the back-end and provides commands to retrieve and store the data into the database. The web interface of database has been built in ASP. RESULTS AND CONCLUSIONS: RespCanDB is expected to contribute to the understanding of scientific community regarding respiratory cancer biology as well as developments of new way of diagnosing and treating respiratory cancer. Currently, the database consist the oncogenomic information of lung cancer, laryngeal cancer, and nasopharyngeal cancer. Data for other cancers, such as oral and tracheal cancers, will be added in the near future. The URL of RespCanDB is http://ridb.subdic-bioinformatics-nitrr.in/.