Influence of Random Measurement Error on Estimated Rates of Headache Chronification and Remission

2013 ◽  
Vol 53 (6) ◽  
pp. 920-929 ◽  
Author(s):  
Timothy T. Houle ◽  
Dana P. Turner ◽  
Todd A. Smitherman ◽  
Donald B. Penzien ◽  
Richard B. Lipton
Keyword(s):  
Measurement Error ◽  
Random Measurement Error ◽  
Random Measurement

scholarly journals Validation of a new signal processing scheme for the MST radar at Aberystwyth

2008 ◽  
Vol 26 (11) ◽  
pp. 3253-3268 ◽  
Author(s):  
D. A. Hooper ◽  
J. Nash ◽  
T. Oakley ◽  
M. Turp
Keyword(s):  
Signal Processing ◽  
Measurement Error ◽  
Gravity Wave ◽  
Wave Activity ◽  
Horizontal Wind ◽  
Time Averaging ◽  
Random Measurement Error ◽  
Processing Scheme ◽  
Random Measurement ◽  
Short Period

Abstract. This paper describes a new signal processing scheme for the 46.5 MHz Doppler Beam Swinging wind-profiling radar at Aberystwyth, in the UK. Although the techniques used are similar to those already described in literature – i.e. the identification of multiple signal components within each spectrum and the use of radial- and time-continuity algorithms for quality-control purposes – it is shown that they must be adapted for the specific meteorological environment above Aberystwyth. In particular they need to take into account the three primary causes of unwanted signals: ground clutter, interference, and Rayleigh scatter from hydrometeors under stratiform precipitation conditions. Attention is also paid to the fact that short-period gravity-wave activity can lead to an invalidation of the fundamental assumption of the wind field remaining stationary over the temporal and spatial scales encompassed by a cycle of observation. Methods of identifying and accounting for such conditions are described. The random measurement error associated with horizontal wind components is estimated to be 3.0–4.0 m s−1 for single cycle data. This reduces to 2.0–3.0 m s−1 for data averaged over 30 min. The random measurement error associated with vertical wind components is estimated to be 0.2–0.3 m s−1. This cannot be reduced by time-averaging as significant natural variability is expected over intervals of just a few minutes under conditions of short-period gravity-wave activity.

Effects of Random Measurement Error on Lung Cancer Screening Decisions

CHEST Journal ◽  
2020 ◽  
Author(s):  
Tanner J. Caverly ◽  
Xuefei Zhang ◽  
Rodney A. Hayward ◽  
Ji Zhu ◽  
Akbar K. Waljee
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Measurement Error ◽  
Lung Cancer Screening ◽  
Random Measurement Error ◽  
Random Measurement

scholarly journals Sampling strategies and post-processing methods for increasing the time resolution of organic aerosol measurements requiring long sample collection times

2016 ◽  
Author(s):  
R. L. Modini ◽  
S. Takahama
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Resolution ◽  
Urban Site ◽  
Sampling Strategies ◽  
Post Processing ◽  
Sample Collection ◽  
Random Measurement Error ◽  
Processing Methods ◽  
Random Measurement

Abstract. The composition and properties of atmospheric Organic Aerosols (OAs) change on timescales of minutes to hours. However, some important OA characterization techniques typically require greater than a few hours of sample collection time (e.g. Fourier Transform Infrared (FTIR) spectroscopy). In this study we have performed numerical modeling to investigate and compare sample collection strategies and post-processing methods for increasing the time resolution of OA measurements requiring long sample collection times. Specifically, we modeled the measurement of Hydrocarbon-like OA (HOA) and Oxygenated OA (OOA) concentrations at a polluted urban site in Mexico City, and investigated how to construct hourly-resolved time series from samples collected for 4, 6, and 8 h. We modeled two sampling strategies – sequential and staggered sampling – and a range of post-processing methods including interpolation and deconvolution. The results indicated that relative to the more sophisticated and costly staggered sampling methods, linear interpolation between sequential measurements is a surprisingly effective method for increasing time resolution. Additional error can be added to a time series constructed in this manner if a suboptimal sequential sampling schedule is chosen. Staggering measurements is one way to avoid this effect. There is little to be gained from deconvolving staggered measurements, except at very low values of random measurement error (< 5 %). Assuming 20 % random measurement error, one can expect average recovery errors of 1.33–2.81 μg m−3 when using 4–8 h long sequential and staggered samples to measure time series of concentration values ranging from 0.13–29.16 μg m−3. For 4 h samples, 19–47 % of this total error can be attributed to the process of increasing time resolution alone, depending on the method used, meaning that measurement precision would only be improved by 0.30–0.75 μg m−3 if samples could be collected over 1 h instead of 4 h. Devising a suitable sampling strategy and post-processing method is a good approach for increasing the time resolution of measurements requiring long sample collection times.

scholarly journals Sampling strategies and post-processing methods for increasing the time resolution of organic aerosol measurements requiring long sample-collection times

2016 ◽  
Vol 9 (7) ◽  
pp. 3337-3354
Author(s):  
Rob L. Modini ◽  
Satoshi Takahama
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Resolution ◽  
Urban Site ◽  
Sampling Strategies ◽  
Post Processing ◽  
Sample Collection ◽  
Random Measurement Error ◽  
Processing Methods ◽  
Random Measurement

Abstract. The composition and properties of atmospheric organic aerosols (OAs) change on timescales of minutes to hours. However, some important OA characterization techniques typically require greater than a few hours of sample-collection time (e.g., Fourier transform infrared (FTIR) spectroscopy). In this study we have performed numerical modeling to investigate and compare sample-collection strategies and post-processing methods for increasing the time resolution of OA measurements requiring long sample-collection times. Specifically, we modeled the measurement of hydrocarbon-like OA (HOA) and oxygenated OA (OOA) concentrations at a polluted urban site in Mexico City, and investigated how to construct hourly resolved time series from samples collected for 4, 6, and 8 h. We modeled two sampling strategies – sequential and staggered sampling – and a range of post-processing methods including interpolation and deconvolution. The results indicated that relative to the more sophisticated and costly staggered sampling methods, linear interpolation between sequential measurements is a surprisingly effective method for increasing time resolution. Additional error can be added to a time series constructed in this manner if a suboptimal sequential sampling schedule is chosen. Staggering measurements is one way to avoid this effect. There is little to be gained from deconvolving staggered measurements, except at very low values of random measurement error (< 5 %). Assuming 20 % random measurement error, one can expect average recovery errors of 1.33–2.81 µg m−3 when using 4–8 h-long sequential and staggered samples to measure time series of concentration values ranging from 0.13–29.16 µg m−3. For 4 h samples, 19–47 % of this total error can be attributed to the process of increasing time resolution alone, depending on the method used, meaning that measurement precision would only be improved by 0.30–0.75 µg m−3 if samples could be collected over 1 h instead of 4 h. Devising a suitable sampling strategy and post-processing method is a good approach for increasing the time resolution of measurements requiring long sample-collection times.

Does True Inter-Individual Variability Exist in Individual Responses After Endurance Training?

2018 ◽  
Author(s):  
Simo Lu
Keyword(s):  
Individual Differences ◽  
Measurement Error ◽  
Endurance Training ◽  
Training Group ◽  
Individual Variability ◽  
Work Rate ◽  
Control Group ◽  
Random Measurement Error ◽  
Random Measurement ◽  
The Impact

Introduction: Despite the apparent existence of individual responses, it remains unknown whether the variability observed in peak oxygen consumption (VO2peak) and work rate at onset of blood lactate (OBLAWR) response following exercise training reflects true inter-individual differences. To date, few studies include a non-exercise control group to determine the impact of random/measurement error on the variability associated with VO2peak and OBLAWR responses to endurance training. Therefore, the purpose of this study was to determine whether true individual differences exist in responses to training by assessing whether the variability in VO2peak and OBLAWR responses following training exceeded the variability in a non-training control group. Methods: 16 recreationally active males completed two incremental ramp tests to determine VO2peak and OBLAWR. Participants were assigned into the control group (n = 7) or the training group (n = 9; endurance training: 30 minutes of 65% of work rate at VO2peak, four times per week) in a manner to counterbalance baseline VO2peak measures. Results: VO2peak increased significantly (p < 0.05) (+338 ± 416.2 mL/min/kg) and OBLAWR (+32.1 ± 29.2 W) increased following endurance training. The SD in change scores was greater in the training group for VO2peak and OBLAWR than the parallel control group. Specifically, this resulted in large and moderately-large effect sizes at respective values of 0.6 for VO2peak and 0.5 for OBLAWR. Conclusion: Although these preliminary results may suggest that the variability in VO2peak and OBLAWR responses to endurance training reflect true inter-individual variability beyond random/measurement error, a definitive conclusion can be made upon the completion of the study.

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