468,505 research outputs found
Model adequacy and influential observations
A common characteristic of diagnostic measures on influential observations is the assumption that all relevant regressors are included in the model, and that none of them can be deleted. We review and illustrate a method to detect data points which are influential enough to establish the empirical (in)significance of regressors
Sensitivity of principal Hessian direction analysis
We provide sensitivity comparisons for two competing versions of the
dimension reduction method principal Hessian directions (pHd). These
comparisons consider the effects of small perturbations on the estimation of
the dimension reduction subspace via the influence function. We show that the
two versions of pHd can behave completely differently in the presence of
certain observational types. Our results also provide evidence that outliers in
the traditional sense may or may not be highly influential in practice. Since
influential observations may lurk within otherwise typical data, we consider
the influence function in the empirical setting for the efficient detection of
influential observations in practice.Comment: Published at http://dx.doi.org/10.1214/07-EJS064 in the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Bayesian look at diagnostics in the univariate linear model.
This paper develops diagnostics for data thought to be generated in accordance with the general univariate linear model. A first set of diagnostics is developed by considering posterior probabilities of models that dictate which of k observations form a sample of n observations (kspurious and outlying observations; posteriors of models; leverage; Kullback-Leibler measures; outlying and influential observations;
Extreme Value Theory Filtering Techniques for Outlier Detection
We introduce asymptotic parameter-free hypothesis tests based on extreme value theory to detect outlying observations in finite samples. Our tests have nontrivial power for detecting outliers for general forms of the parent distribution and can be implemented when this is unknown and needs to be estimated. Using these techniques this article also develops an algorithm to uncover outliers masked by the presence of influential observations
Influence diagnostics in exponentiated-Weibull regression models with censored data
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influential observations in data sets. In this paper, we follow the local influence approach (Cook 1986) in detecting influential observations with exponentiated-Weibull regression models. The relevance of the approach is illustrated with a real data set, where it is shown that by removing the most influential observations, there is a change in the decision about which model fits the data better
Influence diagnostics in exponentiated-Weibull regression models with censored data.
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influential observations in data sets. In this paper, we follow the local influence approach (Cook 1986) in detecting
influential observations with exponentiated-Weibull regression models. The relevance of the approach is illustrated with a real data set, where it is shown that by removing the most influential observations, there is a change in the decision about which model fits the data better.Peer Reviewe
Social Learning over Weakly-Connected Graphs
In this paper, we study diffusion social learning over weakly-connected
graphs. We show that the asymmetric flow of information hinders the learning
abilities of certain agents regardless of their local observations. Under some
circumstances that we clarify in this work, a scenario of total influence (or
"mind-control") arises where a set of influential agents ends up shaping the
beliefs of non-influential agents. We derive useful closed-form expressions
that characterize this influence, and which can be used to motivate design
problems to control it. We provide simulation examples to illustrate the
results.Comment: To appear in 2017 in the IEEE Transactions on Signal and Information
Processing over Network
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