دانشگاه علامه طباطبایی

  اخبار و اعلانات

 آمار

تعداد نشریات57
تعداد شماره‌ها1,930
تعداد مقالات15,373
تعداد مشاهده مقاله36,198,860
تعداد دریافت فایل اصل مقاله21,845,525
Shirazi, Esmaeil. (1401). Nonparametric Wavelet Quantile Density Estimations Based on Biased Data. سامانه مدیریت نشریات علمی, 1(1), 143-158. doi: 10.22054/jcsm.2018.34089.1009
Esmaeil Shirazi. "Nonparametric Wavelet Quantile Density Estimations Based on Biased Data". سامانه مدیریت نشریات علمی, 1, 1, 1401, 143-158. doi: 10.22054/jcsm.2018.34089.1009
Shirazi, Esmaeil. (1401). 'Nonparametric Wavelet Quantile Density Estimations Based on Biased Data', سامانه مدیریت نشریات علمی, 1(1), pp. 143-158. doi: 10.22054/jcsm.2018.34089.1009
Shirazi, Esmaeil. Nonparametric Wavelet Quantile Density Estimations Based on Biased Data. سامانه مدیریت نشریات علمی, 1401; 1(1): 143-158. doi: 10.22054/jcsm.2018.34089.1009

Nonparametric Wavelet Quantile Density Estimations Based on Biased Data

Journal of Data Science and Modeling
مقاله 10، دوره 1، شماره 1، اسفند 2022، صفحه 143-158    اصل مقاله (315.44 K)
نوع مقاله: Research Manuscript
شناسه دیجیتال (DOI): 10.22054/jcsm.2018.34089.1009
نویسنده
Esmaeil Shirazi*
Department of Statistics, Faculty of Science, Gonbad Kavous University, Gonbad Kavous 4971799151, Iran.
چکیده
Estimation of a quantile density function from biased data is a frequent problem in industrial life testing
experiments and medical studies.
The estimation of a quantile density function in the biased nonparametric regression model is inves-
tigated. We propose and develop a new wavelet-based methodology for this problem. In particular, an
adaptive hard thresholding wavelet estimator is constructed. Under mild assumptions on the model, we
prove that it enjoys powerful mean integrated squared error properties over Besov balls. The performance
of proposed estimator is investigated by a numerical study.
In this study, we develop two types of wavelet estimators for the quantile density function when data
comes from a biased distribution function. Our wavelet hard thresholding estimator which is introduced
as a nonlinear estimator, has the feature to be adaptive according to q(x). We show that these estimators
attain optimal and nearly optimal rates of convergence over a wide range of Besov function classes.
کلیدواژه‌ها
Adaptivity؛ Biased Data؛ Quantile density estimation؛ Wavelets
آمار
تعداد مشاهده مقاله: 254
تعداد دریافت فایل اصل مقاله: 341


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب
login