Asymptotics for in-sample density forecasting

DOI10.1214/14-AOS1288zbMath1312.62050arXiv1503.05033OpenAlexW3101764794MaRDI QIDQ2343957

Yanyan Li

Publication date: 11 May 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.05033

zbMATH Keywords

kernel smoothingdensity estimationchain ladderbackfitting

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (8)

Smooth Backfitting of Proportional Hazards With Multiplicative Components ⋮ Estimation of a semiparametric multiplicative density model ⋮ Calendar effect and in-sample forecasting ⋮ A comparison of in-sample forecasting methods ⋮ Continuous chain-ladder with paid data ⋮ Generalised additive dependency inflated models including aggregated covariates ⋮ Unnamed Item ⋮ Over-Dispersed Age-Period-Cohort Models

Uses Software

• DCL

Cites Work

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• The existence and asymptotic properties of a backfitting projection algorithm under weak conditions
• Flexible generalized varying coefficient regression models
• Identification of the age-period-cohort model and the extended chain-ladder model
• Backfitting and smooth backfitting for additive quantile models
• A simple smooth backfitting method for additive models
• Estimation in additive models with highly or non-highly correlated covariates
• Fitting a bivariate additive model by local polynomial regression
• A bandwidth selector for local linear density estimators
• In-sample forecasting applied to reserving and mesothelioma mortality
• The critical exponent conjecture for powers of doubly nonnegative matrices
• Smooth backfitting in generalized additive models
• Bandwidth selection for smooth backfitting in additive models
• Generalised structured models
• Time-Varying Additive Models for Longitudinal Data
• Double Chain Ladder
• An Optimization Interpretation of Integration and Back-Fitting Estimators for Separable Nonparametric Models
• Multivariate Boundary Kernels from Local Linear Estimation
• Smooth Backfitting in Practice
• Age-Specific Incidence and Prevalence: A Statistical Perspective
• A kernel method of estimating structured nonparametric regression based on marginal integration

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