Displaying similar documents to “A statistical variance components framework for mapping imprinted quantitative trait locus in experimental crosses.”

Linear comparative calibration with correlated measurements

Gejza Wimmer, Viktor Witkovský (2007)

Kybernetika

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The paper deals with the linear comparative calibration problem, i. e. the situation when both variables are subject to errors. Considered is a quite general model which allows to include possibly correlated data (measurements). From statistical point of view the model could be represented by the linear errors-in-variables (EIV) model. We suggest an iterative algorithm for estimation the parameters of the analysis function (inverse of the calibration line) and we solve the problem of...

Quantile plots in the analysis of heteroscedastic models.

Montserrat Pepió Viñals, Carlos Polo Miranda (1992)

Qüestiió

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Recent developments in quality engineering methods have led to considerable interest in the analysis of variance, buiding a dispersion model, identifying important effects from replicated experiments and checking for significance by means of a half-normal plot. A methodology based on a chi-squared quantile plot is presented here for checking first the presence of heteroscedasticity, outliers and other data peculiarities, and after the estimation stage a new stepwise procedure tests for...

On equivalence and bioequivalence testing.

Jordi Ocaña, M. Pilar Sánchez O., Álex Sánchez, Josep Lluís Carrasco (2008)

SORT

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Equivalence testing is the natural approach to many statistical problems. First, its main application, bioequivalence testing, is reviewed. The basic concepts of bioequivalence testing (2×2 crossover designs, TOST, interval inclusion principle, etc.) and its problems (TOST biased character, the carryover problem, etc.) are considered. Next, equivalence testing is discussed more generally. Some applications and methods are reviewed and the relation of equivalence testing and distance-based...

Product expenditure patterns in the ECPF survey: an analysis using multiple group latent-variables models.

Eva Ventura, Albert Satorra (2001)

Qüestiió

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Using data form the Spanish household budget survey, we investigate some aspects of household heterogeneity on several product expenditures. We adopt a latent-variable model approach to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the...

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...

Factor analysis and information criteria.

Michele Costa (1996)

Qüestiió

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In this paper the research of the true number of latent factors in exploratoty factor analysis model is studied through a comparison between the log likelihood ratio test statistics, the information criteria of Akaike, Schwarz and Hannah-Quinn and a procedure of cross-validation. In a simulation study the a priori knowledge of the exact factor structure is used to evaluate the goodness of the different methods.

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...