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Analyzing time-ordered event data with missed observations. / Dokter, Adriaan M. (Corresponding author); van Loon, E. Emiel; Fokkema, Wimke; Lameris, Thomas K.; Nolet, Bart A.; van der Jeugd, Henk P.

In: Ecology and Evolution, Vol. 7, No. 18, 2017, p. 7362-7369.

Research output: Scientific - peer-reviewArticle

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Dokter, Adriaan M. (Corresponding author); van Loon, E. Emiel; Fokkema, Wimke; Lameris, Thomas K.; Nolet, Bart A.; van der Jeugd, Henk P. / Analyzing time-ordered event data with missed observations.

In: Ecology and Evolution, Vol. 7, No. 18, 2017, p. 7362-7369.

Research output: Scientific - peer-reviewArticle

BibTeX

@article{954a04e8a9844b69b360056b6d139edb,
title = "Analyzing time-ordered event data with missed observations",
abstract = "A common problem with observational datasets is that not all events of interest may be detected. For example, observing animals in the wild can difficult when animals move, hide, or cannot be closely approached. We consider time series of events recorded in conditions where events are occasionally missed by observers or observational devices. These time series are not restricted to behavioral protocols, but can be any cyclic or recurring process where discrete outcomes are observed. Undetected events cause biased inferences on the process of interest, and statistical analyses are needed that can identify and correct the compromised detection processes. Missed observations in time series lead to observed time intervals between events at multiples of the true inter-event time, which conveys information on their detection probability. We derive the theoretical probability density function for observed intervals between events that includes a probability of missed detection. Methodology and software tools are provided for analysis of event data with potential observation bias and its removal. The methodology was applied to simulation data and a case study of defecation rate estimation in geese, which is commonly used to estimate their digestive throughput and energetic uptake, or to calculate goose usage of a feeding site from dropping density. Simulations indicate that at a moderate chance to miss arrival events (p = 0.3), uncorrected arrival intervals were biased upward by up to a factor 3, while parameter values corrected for missed observations were within 1% of their true simulated value. A field case study shows that not accounting for missed observations leads to substantial underestimates of the true defecation rate in geese, and spurious rate differences between sites, which are introduced by differences in observational conditions. These results show that the derived methodology can be used to effectively remove observational biases in time-ordered event data.",
keywords = "fecal output, interval time series, missing data, mixture model, observation protocol, probability of detection, national",
author = "Dokter, {Adriaan M.} and {van Loon}, {E. Emiel} and Wimke Fokkema and Lameris, {Thomas K.} and Nolet, {Bart A.} and {van der Jeugd}, {Henk P.}",
note = "6352, AnE, VT",
year = "2017",
doi = "10.1002/ece3.3281",
volume = "7",
pages = "7362--7369",
journal = "Ecology and Evolution",
issn = "2045-7758",
publisher = "John Wiley and Sons Ltd",
number = "18",

}

RIS

TY - JOUR

T1 - Analyzing time-ordered event data with missed observations

AU - Dokter,Adriaan M.

AU - van Loon,E. Emiel

AU - Fokkema,Wimke

AU - Lameris,Thomas K.

AU - Nolet,Bart A.

AU - van der Jeugd,Henk P.

N1 - 6352, AnE, VT

PY - 2017

Y1 - 2017

N2 - A common problem with observational datasets is that not all events of interest may be detected. For example, observing animals in the wild can difficult when animals move, hide, or cannot be closely approached. We consider time series of events recorded in conditions where events are occasionally missed by observers or observational devices. These time series are not restricted to behavioral protocols, but can be any cyclic or recurring process where discrete outcomes are observed. Undetected events cause biased inferences on the process of interest, and statistical analyses are needed that can identify and correct the compromised detection processes. Missed observations in time series lead to observed time intervals between events at multiples of the true inter-event time, which conveys information on their detection probability. We derive the theoretical probability density function for observed intervals between events that includes a probability of missed detection. Methodology and software tools are provided for analysis of event data with potential observation bias and its removal. The methodology was applied to simulation data and a case study of defecation rate estimation in geese, which is commonly used to estimate their digestive throughput and energetic uptake, or to calculate goose usage of a feeding site from dropping density. Simulations indicate that at a moderate chance to miss arrival events (p = 0.3), uncorrected arrival intervals were biased upward by up to a factor 3, while parameter values corrected for missed observations were within 1% of their true simulated value. A field case study shows that not accounting for missed observations leads to substantial underestimates of the true defecation rate in geese, and spurious rate differences between sites, which are introduced by differences in observational conditions. These results show that the derived methodology can be used to effectively remove observational biases in time-ordered event data.

AB - A common problem with observational datasets is that not all events of interest may be detected. For example, observing animals in the wild can difficult when animals move, hide, or cannot be closely approached. We consider time series of events recorded in conditions where events are occasionally missed by observers or observational devices. These time series are not restricted to behavioral protocols, but can be any cyclic or recurring process where discrete outcomes are observed. Undetected events cause biased inferences on the process of interest, and statistical analyses are needed that can identify and correct the compromised detection processes. Missed observations in time series lead to observed time intervals between events at multiples of the true inter-event time, which conveys information on their detection probability. We derive the theoretical probability density function for observed intervals between events that includes a probability of missed detection. Methodology and software tools are provided for analysis of event data with potential observation bias and its removal. The methodology was applied to simulation data and a case study of defecation rate estimation in geese, which is commonly used to estimate their digestive throughput and energetic uptake, or to calculate goose usage of a feeding site from dropping density. Simulations indicate that at a moderate chance to miss arrival events (p = 0.3), uncorrected arrival intervals were biased upward by up to a factor 3, while parameter values corrected for missed observations were within 1% of their true simulated value. A field case study shows that not accounting for missed observations leads to substantial underestimates of the true defecation rate in geese, and spurious rate differences between sites, which are introduced by differences in observational conditions. These results show that the derived methodology can be used to effectively remove observational biases in time-ordered event data.

KW - fecal output

KW - interval time series

KW - missing data

KW - mixture model

KW - observation protocol

KW - probability of detection

KW - national

U2 - 10.1002/ece3.3281

DO - 10.1002/ece3.3281

M3 - Article

VL - 7

SP - 7362

EP - 7369

JO - Ecology and Evolution

T2 - Ecology and Evolution

JF - Ecology and Evolution

SN - 2045-7758

IS - 18

ER -

ID: 5266129