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Displaying similar documents to “On the logical development of statistical models.”

Pattern-mixture models

Geert Molenberghs, Herbert Thijs, Bart Michiels, Geert Verbeke, Michael G. Kenward (2004)

Journal de la société française de statistique

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An evaluation of the efficiency of plant protection products via nonlinear statistical methods – a simulation study

Ewa Skotarczak, Ewa Bakinowska, Kamila Tomaszyk (2014)

Biometrical Letters

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A nonlinear statistical approach was used to evaluate the efficiency of plant protection products. The methodology presented can be implemented when the observations in an experiment are recorded as success or failure. This occurs, for example, when following the application of a herbicide or pesticide, a single weed or insect is classified as alive (failure) or dead (success). Then a higher probability of success means a higher efficiency of the tested product. Using simulated data...

Bayesian inference in applied statistics.

Arthur P. Dempster (1980)

Trabajos de Estadística e Investigación Operativa

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The task of assessing posterior distributions from noisy empirical data imposes difficult requirements of modelling, computing and assessing sensitivity to model choice. Seasonal analysis of economic time series is used to illustrate ways of approaching such difficulties.

Stochastic models and statistical inference for plant pollen dispersal

Catherine Laredo, Agnès Grimaud (2007)

Journal de la société française de statistique

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Modelling pollen dispersal is essential to make predictions of cross-pollination rates in various environmental conditions between plants of a cultivated species. An important tool for studying this problem is the “individual pollen dispersal function” or “kernel dispersal”. Various models for airborne pollen dispersal are developed. These models are based on assumptions about wind directionality, gravity, settling velocity and may integrate other biological or external parameters. Some...

Bayesian methods in hydrology: a review.

David Ríos Insua, Raquel Montes Díez, Jesús Palomo Martínez (2002)

RACSAM

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Hydrology and water resources management are inherently affected by uncertainty in many of their involved processes, including inflows, rainfall, water demand, evaporation, etc. Statistics plays, therefore, an essential role in their study. We review here some recent advances within Bayesian statistics and decision analysis which will have a profound impact in these fields.

A model for credit scoring: an application of discriminant analysis.

Manuel Artís, Montserrat Guillén, José M.ª Martínez (1994)

Qüestiió

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The application of statistical techniques in decision making, and more specifically for classification requirements, has proved to be adequate in the context of financial problems. In this study, we present the methodology used and the results obtained in the elaboration of a decision-support system for credit assignment. The problem was to provide an automatic tool for a Spanish financial institution that needed to quantify and analyse credit applications from clients. Firstly, we shall...

A Bayesian Spatial Mixture Model for FMRI Analysis

Geliazkova, Maya (2010)

Serdica Journal of Computing

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We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics...