Three new residual error models for population PK/PD analyses

1995 ◽  
Vol 23 (6) ◽  
pp. 651-672 ◽  
Author(s):  
Mats O. Karlsson ◽  
Stuart L. Beal ◽  
Lewis B. Sheiner
Keyword(s):  
Residual Error ◽  
Error Models ◽  
Population Pk

scholarly journals An advanced residual error model for tropospheric delay estimation

GPS Solutions ◽  
2020 ◽  
Vol 24 (4) ◽  
Author(s):  
Szabolcs Rózsa ◽  
Bence Ambrus ◽  
Ildikó Juni ◽  
Pieter Bastiaan Ober ◽  
Máté Mile
Keyword(s):  
Geographical Location ◽  
Residual Error ◽  
Global Navigation Satellite Systems ◽  
Limiting Factors ◽  
Extreme Value Analysis ◽  
Tropospheric Delay ◽  
International Gnss Service ◽  
Value Analysis ◽  
Error Models ◽  
Satellite Systems

Abstract Global navigation satellite systems (GNSS) are widely used for safety-of-life positioning applications. Such applications require high integrity, availability, and continuity of the positioning service. Integrity is assessed by the definition of a protection level, which is an estimation of the maximum positioning error at extremely low probability levels. The emergence of multi-frequency civilian signals and the availability of satellite-based augmentation systems improve the modeling of ionospheric disturbances considerably. As a result, in many applications the tropospheric delay tends to become one of the limiting factors of positioning—especially at low elevation angles. The currently adopted integrity concepts employ a global constant to model the variance of the residual tropospheric delay error. We introduce a new approach to derive residual tropospheric delay error models using the extreme value analysis technique. Seventeen years of global numerical weather model fields are analyzed, and new residual error models are derived for some recently developed tropospheric delay models. Our approach provides models that consider both the geographical location and the seasonal variation of meteorological parameters. Our models are validated with a 17-year-long time series of zenith tropospheric delay estimates as provided by the International GNSS Service. The results show that the developed models are still conservative, while the maximal residual error of the tropospheric delay is still improved by 39–55%. This improvement yields higher service availability and continuity in safety-of-life applications of GNSS.

scholarly journals Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts

2018 ◽  
Author(s):  
Fitsum Woldemeskel ◽  
David McInerney ◽  
Julien Lerat ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
...  
Keyword(s):  
Skill Score ◽  
Climatic Conditions ◽  
Residual Error ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Processing Scheme ◽  
Error Models ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Forecasting System

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates three residual error models based on the logarithmic (Log), log-sinh (Log-Sinh) and Box-Cox with λ = 0.2 (BC0.2) transformation schemes and identifies the best performing scheme for post-processing monthly and seasonal (3-months) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau’s operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing using the three residual error models substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme significantly outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh error models. This study is significant because the reliable and sharper forecasts obtained using the BC0.2 post-processing scheme will help water managers and users of the forecasting service to make better-informed decisions in planning and management of water resources.

Identification in Dynamic Shock-Error Models

1979 ◽  
Author(s):  
Agustin Maravall ◽  
Klaus Neumann ◽  
Ulrich Steinhardt
Keyword(s):  
Error Models

Anti-interference method based on TDOA residual error in multilateration system

2013 ◽  
Vol 33 (5) ◽  
pp. 1470-1472
Author(s):  
Yu LU ◽  
Honggang WU ◽  
Zili XU
Keyword(s):  
Residual Error ◽  
Interference Method

Rapid Style Adaptation Using Residual Error Embedding for Expressive Speech Synthesis

2018 ◽  
Author(s):  
Xixin Wu ◽  
Yuewen Cao ◽  
Mu Wang ◽  
Songxiang Liu ◽  
Shiyin Kang ◽  
...  
Keyword(s):  
Speech Synthesis ◽  
Residual Error ◽  
Expressive Speech

Clinical Pharmacokinetic Studies in Pregnant Women and the Relevance of Pharmacometric Tools

2019 ◽  
Vol 25 (5) ◽  
pp. 483-495 ◽  
Author(s):  
André Dallmann ◽  
Paola Mian ◽  
Johannes Van den Anker ◽  
Karel Allegaert
Keyword(s):  
Pregnant Women ◽  
Clinical Pharmacokinetic ◽  
Individual Variability ◽  
Current Status ◽  
Pharmacokinetic Studies ◽  
Full Potential ◽  
Pbpk Models ◽  
Pregnancy And Postpartum ◽  
Population Pk ◽  
The Impact

Background: In clinical pharmacokinetic (PK) studies, pregnant women are significantly underrepresented because of ethical and legal reasons which lead to a paucity of information on potential PK changes in this population. As a consequence, pharmacometric tools became instrumental to explore and quantify the impact of PK changes during pregnancy. Methods: We explore and discuss the typical characteristics of population PK and physiologically based pharmacokinetic (PBPK) models with a specific focus on pregnancy and postpartum. Results: Population PK models enable the analysis of dense, sparse or unbalanced data to explore covariates in order to (partly) explain inter-individual variability (including pregnancy) and to individualize dosing. For population PK models, we subsequently used an illustrative approach with ketorolac data to highlight the relevance of enantiomer specific modeling for racemic drugs during pregnancy, while data on antibiotic prophylaxis (cefazolin) during surgery illustrate the specific characteristics of the fetal compartments in the presence of timeconcentration profiles. For PBPK models, an overview on the current status of reports and papers during pregnancy is followed by a PBPK cefuroxime model to illustrate the added benefit of PBPK in evaluating dosing regimens in pregnant women. Conclusions: Pharmacometric tools became very instrumental to improve perinatal pharmacology. However, to reach their full potential, multidisciplinary collaboration and structured efforts are needed to generate more information from already available datasets, to share data and models, and to stimulate cross talk between clinicians and pharmacometricians to generate specific observations (pathophysiology during pregnancy, breastfeeding) needed to further develop the field.

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