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- research-articleJuly 2024
Improved memory-type ratio estimator for population mean in stratified random sampling under linear and non-linear cost functions
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 28, Issue 13-14Pages 7739–7754https://doi.org/10.1007/s00500-023-09598-4AbstractThis paper offers an improved memory-type ratio estimator in stratified random sampling under linear and non-linear cost functions. The issue is given as all integer non-linear programming problems (AINLPPs). The sampling properties mainly the ...
- research-articleMarch 2023
Improved Memory Type Product Estimator for Population Mean in Stratified Random Sampling Under Linear Cost Function
AbstractIn this article, we consider the memory type product estimator to estimate the population mean of the study variable in stratified random sampling. The suggested estimators’ bias and mean square error (MSE) expressions for the first order of ...
- research-articleSeptember 2021
- research-articleOctober 2020
K-means Clustering Based Undersampling for Lower Back Pain Data
ICBDT '20: Proceedings of the 3rd International Conference on Big Data TechnologiesPages 53–57https://doi.org/10.1145/3422713.3422725Many people are usually suffered from low back pain(LBP). It is very important to identify the LBP in the early stage. The classification algorithm in machine learning can help us to predict whether a person is suffered from low back pain, but class ...
- research-articleJune 2016
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops
- Gang Zhao,
- Holger Hoffmann,
- Jagadeesh Yeluripati,
- Specka Xenia,
- Claas Nendel,
- Elsa Coucheney,
- Matthias Kuhnert,
- Fulu Tao,
- Julie Constantin,
- Helene Raynal,
- Edmar Teixeira,
- Balázs Grosz,
- Luca Doro,
- Ralf Kiese,
- Henrik Eckersten,
- Edwin Haas,
- Davide Cammarano,
- Belay Kassie,
- Marco Moriondo,
- Giacomo Trombi,
- Marco Bindi,
- Christian Biernath,
- Florian Heinlein,
- Christian Klein,
- Eckart Priesack,
- Elisabet Lewan,
- Kurt-Christian Kersebaum,
- Reimund Rötter,
- Pier Paolo Roggero,
- Daniel Wallach,
- Senthold Asseng,
- Stefan Siebert,
- Thomas Gaiser,
- Frank Ewert
Environmental Modelling & Software (ENMS), Volume 80, Issue CPages 100–112https://doi.org/10.1016/j.envsoft.2016.02.022We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen ...
- articleOctober 2010
An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means
Computers & Geosciences (CGEO), Volume 36, Issue 10Pages 1261–1267https://doi.org/10.1016/j.cageo.2010.04.005Both for mapping and for estimating spatial means of an environmental variable, the accuracy of the result will usually be increased by dispersing the sample locations so that they cover the study area as uniformly as possible. We developed a new R ...
- articleFebruary 2007
Quantile estimation in two-phase sampling
Computational Statistics & Data Analysis (CSDA), Volume 51, Issue 5Pages 2559–2572https://doi.org/10.1016/j.csda.2006.01.002The estimation of quantiles in two-phase sampling with arbitrary sampling design in each of the two phases is investigated. Several ratio and exponentiation type estimators that provide the optimum estimate of a quantile based on an optimum exponent @a ...
- articleFebruary 2006
Ratio estimators for the population variance in simple and stratified random sampling
Applied Mathematics and Computation (APMC), Volume 173, Issue 2Pages 1047–1059https://doi.org/10.1016/j.amc.2005.04.032We propose some ratio-type variance estimators using ratio estimators for the population mean in literature. We obtain mean square error (MSE) equations of proposed estimators and show that proposed estimators are more efficient than the traditional ...
- articleMay 2005
Indirect methods of imputation of missing data based on available units
Applied Mathematics and Computation (APMC), Volume 164, Issue 1Pages 249–261https://doi.org/10.1016/j.amc.2004.04.102One of the most difficult problems confronting investigators who analyze data from surveys is how to treat missing data. Many statistical procedures cannot be used immediately if any values are missing. Imputation of missing data before starting ...