Search results for " linear regression"
showing 10 items of 97 documents
Application of molecular topology to the prediction of antiparasitic activity against Giardia intestinalis and Trichomonas vaginalis of 2-Acylamino-n…
2020
Giardia intestinalis y Trichomonas vaginalis destacan por su importancia clínica. G. intestinalis causa la giardiosis, una parasitosis de gran importancia epidemiológica y clínica por presentar una elevada prevalencia. T. vaginalis causa la tricomoniasis, la enfermedad de transmisión sexual (ETS) no viral con mayor incidencia del mundo. Ambas parasitosis comparten el mismo tratamiento farmacológico: los nitroimidazoles. Se ha aplicado la topología molecular en la búsqueda de derivados del 2-Acylamino-nitro-1,3-tiazol con actividad antiparasitaria frente a G. intestinalis y T. vaginalis . Con el análisis lineal discriminante se obtuvo un modelo capaz de clasificar correctamente el 92,85 % de…
Multiple linear regression analysis of RF values of chlorinated catechols and guaiacols
1981
The multiple linear regression analysis of the RF values of chlorinated catechols and guaiacols has been carried out. The resolved terms, in the regression equation have been used to explain the relative mobility of chlorinated compounds to the reference compound (catechol or guaiacol). The best correlations have been observed for solvent systems which give the greatest standard deviations and relative differences between the RF values. A good correlation between the standard deviation of the RF values and the term which represents the effect of the chlorine atom ortho to the hydroxyl group(s) have also been observed.
A Bayesian analysis of classical hypothesis testing
1980
The procedure of maximizing the missing information is applied to derive reference posterior probabilities for null hypotheses. The results shed further light on Lindley’s paradox and suggest that a Bayesian interpretation of classical hypothesis testing is possible by providing a one-to-one approximate relationship between significance levels and posterior probabilities.
What Bayesians Expect of Each Other
1991
Abstract Our goal is to study general properties of one Bayesian's subjective beliefs about the behavior of another Bayesian's subjective beliefs. We consider two Bayesians, A and B, who have different subjective distributions for a parameter θ, and study Bayesian A's expectation of Bayesian B's posterior distribution for θ given some data Y. We show that when θ can take only two values, Bayesian A always expects Bayesian B's posterior distribution to lie between the prior distributions of A and B. Conditions are given under which a similar result holds for an arbitrary real-valued parameter θ. For a vector parameter θ we present useful expressions for the mean vector and covariance matrix …
An introduction to Bayesian reference analysis: inference on the ratio of multinomial parameters
1998
This paper offers an introduction to Bayesian reference analysis, often described as the more successful method to produce non-subjective, model-based, posterior distributions. The ideas are illustrated in detail with an interesting problem, the ratio of multinomial parameters, for which no model-based Bayesian analysis has been proposed. Signposts are provided to the huge related literature.
A Software Tool for the Exponential Power Distribution: The normalp Package
2005
In this paper we present the normalp package, a package for the statistical environment R that has a set of tools for dealing with the exponential power distribution. In this package there are functions to compute the density function, the distribution function and the quantiles from an exponential power distribution and to generate pseudo-random numbers from the same distribution. Moreover, methods concerning the estimation of the distribution parameters are described and implemented. It is also possible to estimate linear regression models when we assume the random errors distributed according to an exponential power distribution. A set of functions is designed to perform simulation studi…
Methods and Tools for Bayesian Variable Selection and Model Averaging in Normal Linear Regression
2018
In this paper, we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available R-packages, summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed (can) lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.
Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models
2013
Summary Sparsity is an essential feature of many contemporary data problems. Remote sensing, various forms of automated screening and other high throughput measurement devices collect a large amount of information, typically about few independent statistical subjects or units. In certain cases it is reasonable to assume that the underlying process generating the data is itself sparse, in the sense that only a few of the measured variables are involved in the process. We propose an explicit method of monotonically decreasing sparsity for outcomes that can be modelled by an exponential family. In our approach we generalize the equiangular condition in a generalized linear model. Although the …
Simulation in the Simple Linear Regression Model
2002
Summary This article presents an activity which simulates the linear regression model in order to verify the probabilistic behaviour of the resulting least-squares statistics in practice.
Linear and ellipsoidal restrictions in linear regression
1991
The problem of combining linear and ellipsoidal restrictions in linear regression is investigated. Necessary and sufficient conditions for compactness of the restriction set are proved assuring the existence of a minimax estimator. When the restriction set is not compact a minimax estimator may still exist for special loss functions arid regression designs