scholarly journals Measurement error in two-stage analyses, with application to air pollution epidemiology

2013 ◽  
Vol 24 (8) ◽  
pp. 501-517 ◽  
Author(s):  
Adam A. Szpiro ◽  
Christopher J. Paciorek
Keyword(s):  
Air Pollution ◽  
Measurement Error ◽  
Two Stage ◽  
Air Pollution Epidemiology

scholarly journals Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Gretchen T Goldman ◽  
James A Mulholland ◽  
Armistead G Russell ◽  
Matthew J Strickland ◽  
Mitchel Klein ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Measurement Error ◽  
Exposure Measurement ◽  
Error Type ◽  
Exposure Measurement Error ◽  
Air Pollution Epidemiology ◽  
Time Series Studies

scholarly journals Confounding and exposure measurement error in air pollution epidemiology

2011 ◽  
Vol 5 (2) ◽  
pp. 203-216 ◽  
Author(s):  
Lianne Sheppard ◽  
Richard T. Burnett ◽  
Adam A. Szpiro ◽  
Sun-Young Kim ◽  
Michael Jerrett ◽  
...  
Keyword(s):  
Air Pollution ◽  
Measurement Error ◽  
Exposure Measurement ◽  
Exposure Measurement Error ◽  
Air Pollution Epidemiology

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

scholarly journals Correction to: Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Measurement Error ◽  
Stage Model ◽  
Two Stage ◽  
Gps Measurement ◽  
Behavioral States

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