A study on collective model-based floating population missing data simulation

2014 ◽  
Author(s):  
Zheng Wang ◽  
Yingjie Wang
Keyword(s):  
Missing Data ◽  
Collective Model ◽  
Floating Population ◽  
Data Simulation ◽  
Model Based

scholarly journals Missing data simulation inside flow rate time-series using multiple-point statistics

2016 ◽  
Vol 86 ◽  
pp. 264-276 ◽  
Author(s):  
Fabio Oriani ◽  
Andrea Borghi ◽  
Julien Straubhaar ◽  
Grégoire Mariethoz ◽  
Philippe Renard
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Flow Rate ◽  
Multiple Point ◽  
Data Simulation ◽  
Multiple Point Statistics

scholarly journals Statistical arrival models to estimate missed passage counts at fish weirs

2016 ◽  
Vol 73 (8) ◽  
pp. 1251-1260 ◽  
Author(s):  
Suresh Andrew Sethi ◽  
Catherine Bradley
Keyword(s):  
Missing Data ◽  
Case Studies ◽  
Statistical Approach ◽  
Pacific Salmon ◽  
Linear Interpolation ◽  
Fish Population ◽  
Data Simulation ◽  
Yukon River ◽  
Curve Model ◽  
Selection Of

Missed counts are commonplace when enumerating fish passing a weir. Typically “connect-the-dots” linear interpolation is used to impute missed passage; however, this method fails to characterize uncertainty about estimates and cannot be implemented when the tails of a run are missed. Here, we present a statistical approach to imputing missing passage at weirs that addresses these shortcomings, consisting of a parametric run curve model to describe the smoothed arrival dynamics of a fish population and a process variation model to describe the likelihood of observed data. Statistical arrival models are fit in a Bayesian framework and tested with a suite of missing data simulation trials and against a selection of Pacific salmon (Oncorhynchus spp.) case studies from the Yukon River drainage, Alaska, USA. When compared against linear interpolation, statistical arrival models produced equivalent or better expected accuracy and a narrower range of bias outcomes. Statistical arrival models also successfully imputed missing passage counts for scenarios where the tails of a run were missed.

Coping with missing data in clinical trials: A model-based approach applied to asthma trials

2002 ◽  
Vol 21 (8) ◽  
pp. 1043-1066 ◽  
Author(s):  
James Carpenter ◽  
Stuart Pocock ◽  
Carl Johan Lamm
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Model Based

A model-based approach to the imputation of missing data: Home injury incidences

1989 ◽  
Vol 8 (3) ◽  
pp. 263-266 ◽  
Author(s):  
Judith M. Conn ◽  
Kung-Jong Lui ◽  
Daniel L. McGee
Keyword(s):  
Missing Data ◽  
Model Based ◽  
Home Injury

Missing Data in Model-Based Pharmacometric Applications: Points to Consider

2010 ◽  
Vol 50 (S9) ◽  
pp. 63S-74S ◽  
Author(s):  
Marc R. Gastonguay ◽  
Jonathan L. French ◽  
Daniel F. Heitjan ◽  
James A. Rogers ◽  
Jae Eun Ahn ◽  
...  
Keyword(s):  
Missing Data ◽  
Model Based
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