Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

Fitting Models to Grouped Data

Lysis of grouped and ungrouped streptococci by lysozyme.

Thirty strains of streptococci were tested for lysis with lysozyme, and 29 of these could be lysed by the following method: (i) suspension of the cells to a Klett reading of 200 units (no. 42 filter) in 0.01 m tris(hydroxymethyl)aminomethane buffer, pH 8.2, after washing twice with the buffer; (ii) addition of lysozyme to a final concentration of 250 mug/ml with incubation for 60 min at 37 C; (...

On The Identifiability of Mixture Models from Grouped Samples

Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating mixture models, it is common to make assumptions on the mixture components, s...

Data Resolution Versus Forestry Classification and Modeling

This paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be bet...

Modeling Censored Data Using Mixture Regression Models with an Application to Cattle Production Yields

This research develops a mixture regression model that is shown to have advantages over the classical Tobit model in model fit and predictive tests when data are generated from a two step process. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting the classic Tobit model. A simulated data set is utilized to assess the potential loss in efficien...