Regression diagnostics and robust regression in geothermometer/geobarometer calibration: the garnet-clinopyroxene geothermometer revisited

1985 ◽  
Vol 3 (3) ◽  
pp. 231-243 ◽  
Author(s):  
ROGER POWELL
Keyword(s):  
Robust Regression ◽  
Regression Diagnostics

Reliable Robust Regression Diagnostics

2015 ◽  
Vol 84 (1) ◽  
pp. 99-127 ◽  
Author(s):  
Silvia Salini ◽  
Andrea Cerioli ◽  
Fabrizio Laurini ◽  
Marco Riani
Keyword(s):  
Robust Regression ◽  
Regression Diagnostics

scholarly journals Three contributions to robust regression diagnostics

2015 ◽  
Vol 11 (2) ◽  
pp. 69-78 ◽  
Author(s):  
J. Kalina
Keyword(s):  
Robust Regression ◽  
Null Distribution ◽  
Real Data ◽  
Random Regression ◽  
Diagnostic Tools ◽  
Regression Diagnostics ◽  
Least Trimmed Squares ◽  
Test Statistic ◽  
Regression Methods ◽  
Regression Parameters

Abstract Robust regression methods have been developed not only as a diagnostic tool for standard least squares estimation in statistical and econometric applications, but can be also used as self-standing regression estimation procedures. Therefore, they need to be equipped by their own diagnostic tools. This paper is devoted to robust regression and presents three contributions to its diagnostic tools or estimating regression parameters under non-standard conditions. Firstly, we derive the Durbin-Watson test of independence of random regression errors for the regression median. The approach is based on the approximation to the exact null distribution of the test statistic. Secondly, we accompany the least trimmed squares estimator by a subjective criterion for selecting a suitable value of the trimming constant. Thirdly, we propose a robust version of the instrumental variables estimator. The new methods are illustrated on examples with real data and their advantages and limitations are discussed.

Common Genetic Variants in ABO and CLEC4M Modulate the Pharmacokinetics of Recombinant FVIII in Severe Hemophilia A Patients

2020 ◽  
Vol 120 (10) ◽  
pp. 1395-1406 ◽  
Author(s):  
Iris Garcia-Martínez ◽  
Nina Borràs ◽  
Marta Martorell ◽  
Rafael Parra ◽  
Carme Altisent ◽  
...  
Keyword(s):  
Blood Group ◽  
Half Life ◽  
Robust Regression ◽  
Hemophilia A ◽  
Illumina Miseq ◽  
Activity Levels ◽  
Severe Hemophilia ◽  
Single Nucleotide Variants ◽  
Common Genetic Variants ◽  
Recombinant Fviii

AbstractThe pharmacokinetic (PK) response of severe hemophilia A (HA) patients to infused factor VIII (FVIII) shows substantial variability. Several environmental and genetic factors are associated with changes in FVIII plasma levels and infused FVIII PK. Based on the hypothesis that factors influencing endogenous FVIII can affect FVIII PK, the contribution of single-nucleotide variants (SNVs) in candidate genes was investigated in 51 severe HA patients. The effects of blood group, F8 variant type, von Willebrand factor antigen and activity levels, age, and weight were also explored. The myPKFiT device was used to estimate individual PK parameters, and SNVs and clinically reportable F8 variants were simultaneously analyzed in an Illumina MiSeq instrument, using the microfluidics-based Fluidigm Access Array system. The contribution of SNVs to FVIII half-life and clearance was addressed by robust regression modeling, taking into account other modulators. In line with previous studies, we provide robust evidence that age, body weight, and blood group, as well as SNVs in ABO and CLEC4M, participate in the variability of FVIII PK in HA patients. Main results: each copy of the rs7853989 (ABO) allele increases FVIII half-life by 1.4 hours (p = 0.0131) and decreases clearance by 0.5 mL/h/kg (p = 5.57E-03), whereas each additional rs868875 (CLEC4M) allele reduces FVIII half-life by 1.1 hours (p = 2.90E-05) and increases clearance by 0.3 mL/h/kg (p = 1.01E-03). These results contribute to advancing efforts to improve FVIII replacement therapies by adjusting to each patient's PK profile based on pharmacogenomic data. This personalized medicine will decrease the burden of treatment and maximize the benefits obtained.

Increased sensitivity in neuroimaging analyses using robust regression

NeuroImage ◽  
2005 ◽  
Vol 26 (1) ◽  
pp. 99-113 ◽  
Author(s):  
Tor D. Wager ◽  
Matthew C. Keller ◽  
Steven C. Lacey ◽  
John Jonides
Keyword(s):  
Robust Regression ◽  
Increased Sensitivity

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

2020 ◽  
Vol 32 (3) ◽  
pp. 394-400
Author(s):  
Giovani Trevisan ◽  
Leticia C. M. Linhares ◽  
Bret Crim ◽  
Poonam Dubey ◽  
Kent J. Schwartz ◽  
...  
Keyword(s):  
Robust Regression ◽  
Historical Data ◽  
The United States ◽  
Respiratory Syndrome Virus ◽  
Seasonal Patterns ◽  
Test Results ◽  
Swine Industry ◽  
Cyclic Pattern ◽  
Rna Detection ◽  
Virus Rna

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year’s weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015–2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14–25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14–25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

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