Analysis of logistic growth models

A variety of growth curves have been developed to model both unpredated, intraspecific population dynamics and more general biological growth. Most successful predictive models are shown to be based on extended forms of the classical Verhulst logistic growth equation. We further review and compare several such models and calculate and investigate properties of interest for these. We also identify and detail several previously unreported associated limitations and restrictions. A generalized form of the logistic growth curve is introduced which is shown incorporate these models as special cases. The reported limitations of the generic growth model are shown to be addressed by this new model and similarities between this and the extended growth curves are identified. Several of its properties are also presented. We furthermore show that additional growth characteristics are accommodated by this new model, enabling previously unsupported, untypical population dynamics to be modelled by judicious choice of model parameter values alone

Sparse logistic principal components analysis for binary data

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization--Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS327 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Biomass Production of Selected Energy Plants: Economic Analysis and Logistic Strategies

The objective of this article is the conducting of an analysis of the production of selected energy plants that are already a basic source of agrobiomass in Poland. The analysis looks at environmental aspects and production conditions for biomass designated for energy for the Virginia mallow (Sida hermaphrodita), common osier (Salix viminalis), silver-grass (Miscanthus x giganteus), and switchgrass (Panicum virgatum). What is presented is an economic analysis of the production of selected energy plants, taking into account the costs of establishing plantations and their cost effectiveness. Moreover, logistic strategies for the delivery of biomass intended to secure continuous production of renewable energy as a part of sustainable development is signaled.Celem nieniejszego artykułu było przeprowadzenie analizy produkcji wybranych roślin energetycznych, które w Polsce są już podstawowym źródłem agrobiomasy. W treści analiza zawierała aspekty środowiskowe i uwarunkowania produkcji biomasy na cele energetyczne dla ślazowca pensylwańskiego (Sida hermaphrodita), wierzby wiciowej z rodzaju Salix,i miskanta olbrzymiego (Miscanthus x giganteus) i prosa rózgowatego (Panicum virgatum). Przedstawiono analizę ekonomiczną produkcji wybranych roślin energetycznych z uwzględnieniem kosztów plantacji i ich opłacalności oraz zasygnalizowano strategie logistyczne dla dostaw biomasy w celu zabezpieczenia stałej produkcji energii odnawialnej w zrównoważonym rozwoju