An adaptive nonparametric method in benchmark analysis for bioassay and environmental studies

DOI10.1016/j.spl.2010.08.024zbMath1202.62145OpenAlexW2059097919WikidataQ42580550 ScholiaQ42580550MaRDI QIDQ613190

Lizhen Lin, Rabi N. Bhattacharya

Publication date: 20 December 2010

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3027186

zbMATH Keywords

asymptotic normalitybenchmark analysiseffective dosagemean integrated square errormonotone dose-response curve estimation

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Related Items

Nonparametric Benchmark Dose Estimation with Continuous Dose‐Response Data ⋮ Finite-dimensional approximation of Gaussian processes with linear inequality constraints and noisy observations ⋮ Nonparametric benchmark analysis in risk assessment: a comparative study by simulation and data analysis ⋮ Recent progress in the nonparametric estimation of monotone curves -- with applications to bioassay and environmental risk assessment

Uses Software

• AS 149

Cites Work

• Unnamed Item
• Nonparametric benchmark analysis in risk assessment: a comparative study by simulation and data analysis
• Consistency and asymptotic normality of the estimated effective doses in bioassay
• An Empirical Distribution Function for Sampling with Incomplete Information
• A finite sample comparison of nonparametric estimates of the effective dose in quantal bioassay
• Kernel and Probit Estimates in Quantal Bioassay
• Algorithm AS 149: Amalgamation of Means in the Case of Simple Ordering
• Parametric and nonparametric estimators of ED100α
• A Note on Nonparametric Estimation of the Effective Dose in Quantal Bioassay
• A basic course in probability theory