Bandingkan kaedah

	Penyimpanan Liabiliti Rantaian Tangga (Model Mack)	Inferens Bootstrap	Generalized Least Squares (GLS)	Model Taburan Kerugian
Bidang	Sains Aktuari	Statistik	Statistik	Sains Aktuari
Keluarga	Regression model	Regression model	Regression model	Regression model
Tahun asal	1993	1979	1935	2012
Pengasas	Thomas Mack	Bradley Efron	Alexander Craig Aitken	Klugman, Panjer & Willmot
Jenis	Stochastic loss reserving model	Resampling-based inference	Linear estimator	Parametric probability model
Sumber perintis	Mack, T. (1993). Distribution-free calculation of the standard error of chain ladder reserve estimates. ASTIN Bulletin, 23(2), 213–225.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	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
Alias	Development Factor Method, Link Ratio Method, Loss Development Method, Zincir Merdiven Yöntemi	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	GLS, Aitken estimator, EGLS, feasible GLS	Severity-Frequency Model, Aggregate Loss Model, Claim Size Distribution Model, Hasar Dağılımı Modeli
Berkaitan	3	5	3	3
Ringkasan	Chain-Ladder Reserving is a stochastic actuarial method for estimating outstanding claim liabilities from a run-off triangle of cumulative paid losses. Formalized by Thomas Mack in 1993, it provides distribution-free estimates of reserve amounts along with their standard errors, making it a cornerstone of property-casualty insurance reserving and regulatory practice worldwide.	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.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.	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.