विधियों की तुलना करें

	बूटस्ट्रैप अनुमान	हानि वितरण मॉडल (Loss Distribution Model)
क्षेत्र	सांख्यिकी	बीमांकिक विज्ञान
परिवार	Regression model	Regression model
उद्भव वर्ष	1979	2012
प्रवर्तक	Bradley Efron	Klugman, Panjer & Willmot
प्रकार	Resampling-based inference	Parametric probability model
मौलिक स्रोत	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss Models: From Data to Decisions (4th ed.). Wiley. ISBN: 978-1-118-31532-3
उपनाम	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	Severity-Frequency Model, Aggregate Loss Model, Claim Size Distribution Model, Hasar Dağılımı Modeli
संबंधित	5	3
सारांश	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998, fourth edition 2012), these models underpin premium rating, reserving, reinsurance pricing, and regulatory capital calculations across the insurance and risk-management industries.