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Full-Text Articles in Statistical Models
Accounting For Matching Uncertainty In Photographic Identification Studies Of Wild Animals, Amanda R. Ellis
Accounting For Matching Uncertainty In Photographic Identification Studies Of Wild Animals, Amanda R. Ellis
Theses and Dissertations--Statistics
I consider statistical modelling of data gathered by photographic identification in mark-recapture studies and propose a new method that incorporates the inherent uncertainty of photographic identification in the estimation of abundance, survival and recruitment. A hierarchical model is proposed which accepts scores assigned to pairs of photographs by pattern recognition algorithms as data and allows for uncertainty in matching photographs based on these scores. The new models incorporate latent capture histories that are treated as unknown random variables informed by the data, contrasting past models having the capture histories being fixed. The methods properly account for uncertainty in the matching …
Bayesian Model For Detection Of Outliers In Linear Regression With Application To Longitudinal Data, Zahraa Al-Sharea
Bayesian Model For Detection Of Outliers In Linear Regression With Application To Longitudinal Data, Zahraa Al-Sharea
Graduate Theses and Dissertations
Outlier detection is one of the most important challenges with many present-day applications. Outliers can occur due to uncertainty in data generating mechanisms or due to an error in data recording/processing. Outliers can drastically change the study's results and make predictions less reliable. Detecting outliers in longitudinal studies is quite challenging because this kind of study is working with observations that change over time. Therefore, the same subject can produce an outlier at one point in time produce regular observations at all other time points. A Bayesian hierarchical modeling assigns parameters that can quantify whether each observation is an outlier …