Spline smoothing in small area trend estimation and forecasting

DOI10.1016/j.csda.2009.02.027zbMath1453.62222OpenAlexW2049195574MaRDI QIDQ961826

T. Goicoa, María Durbán, Ana F. Militino, Maria Dolores Ugarte

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.02.027

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08)

Related Items (15)

Small area estimation via unmatched sampling and linking models ⋮ Selection model for domains across time: application to labour force survey by economic activities ⋮ Special issue on small area estimation ⋮ Small area estimation under spatial nonstationarity ⋮ Estimating the percentage of food expenditure in small areas using bias-corrected \(P\)-spline based estimators ⋮ Small area estimators based on restricted mixed models ⋮ Nonparametric tests for semiparametric regression models ⋮ Small area estimation via M-quantile geographically weighted regression ⋮ Small area estimation using a nonparametric model-based direct estimator ⋮ Robust small area estimation under semi‐parametric mixed models ⋮ Evaluating space‐time models for short‐term cancer mortality risk predictions in small areas ⋮ On longitudinal moving average model for prediction of subpopulation total ⋮ Functional Mixed Effects Model for Small Area Estimation ⋮ On Prediction for Correlated Domains in Longitudinal Surveys ⋮ Comments on: ``Single and two-stage cross-sectional and time series benchmarking procedures for small area estimation

Uses Software

• nlme
• SemiPar

Cites Work

• Benchmarked estimates in small areas using linear mixed models with restrictions
• An efficient computing strategy for prediction in mixed linear models
• Fast and compact smoothing on large multidimensional grids
• Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model
• A practical guide to splines
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Some theory for penalized spline generalized additive models
• Mean squared error of empirical predictor.
• Smoothing and mixed models
• Simple Incorporation of Interactions into Additive Models
• Flexible smoothing with P-splines: a unified approach
• ON CONFIDENCE INTERVALS FOR GENERALIZED ADDITIVE MODELS BASED ON PENALIZED REGRESSION SPLINES
• Non-Parametric Small Area Estimation Using Penalized Spline Regression
• Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests Under Nonstandard Conditions
• Best Linear Unbiased Estimation and Prediction under a Selection Model
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines
• Incorporation of Historical Controls Using Semiparametric Mixed Models
• Semiparametric Regression
• Likelihood Ratio Tests in Linear Mixed Models with One Variance Component
• Thin Plate Regression Splines
• Smoothing and forecasting mortality rates
• Exact likelihood ratio tests for penalised splines
• The Estimation of Environmental and Genetic Trends from Records Subject to Culling
• The Estimation of the Mean Squared Error of Small-Area Estimators
• On the impact of bootstrap in survey sampling and small-area estimation
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Spline smoothing in small area trend estimation and forecasting