8 results match your criteria Applied Stochastic Models In Business And Industry[Journal]

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Copula-based robust optimal block designs.

Appl Stoch Models Bus Ind 2020 Jan-Feb;36(1):210-219. Epub 2019 May 30.

Southampton Statistical Sciences Research Institute University of Southampton Southampton UK.

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. Read More

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Weak signals in high-dimension regression: detection, estimation and prediction.

Appl Stoch Models Bus Ind 2019 Mar-Apr;35(2):283-298. Epub 2018 May 25.

Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109 USA.

Regularization methods, including Lasso, group Lasso and SCAD, typically focus on selecting variables with strong effects while ignoring weak signals. This may result in biased prediction, especially when weak signals outnumber strong signals. This paper aims to incorporate weak signals in variable selection, estimation and prediction. Read More

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Mode hunting through active information.

Appl Stoch Models Bus Ind 2019 Mar-Apr;35(2):376-393. Epub 2019 Jan 31.

Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, Ohio.

We propose a new method to find modes based on active information. We develop an algorithm called active information mode hunting (AIMH) that, when applied to the whole space, will say whether there are any modes present where they are. We show AIMH is consistent and, given that information increases where probability decreases, it helps to overcome issues with the curse of dimensionality. Read More

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January 2019

Integrative Interaction Analysis using Threshold Gradient Directed Regularization.

Appl Stoch Models Bus Ind 2019 Mar-Apr;35(2):354-375. Epub 2018 May 29.

School of Statistics, Remin University of China.

For many complex business and industry problems, high-dimensional data collection and modeling have been conducted. It has been shown that interactions may have important implications beyond main effects. The number of unknown parameters in an interaction analysis can be larger or much larger than the sample size. Read More

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Inferring social structure from continuous-time interaction data.

Appl Stoch Models Bus Ind 2018 Mar-Apr;34(2):87-104. Epub 2017 Oct 20.

University of Washington.

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous-time methods for modeling such data are based on point processes and directly model interaction "contagion," whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal-relational point process models for continuous-time event data. Read More

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October 2017

Clinical Trial Design as a Decision Problem.

Appl Stoch Models Bus Ind 2017 May-Jun;33(3):296-301. Epub 2017 Jan 13.

Dept. of Biostatistics, University of Texas, M.D. Anderson Cancer Center.

The intent of this discussion is to highlight opportunities and limitations of utility-based and decision theoretic arguments in clinical trial design. The discussion is based on a specific case study, but the arguments and principles remain valid in general. The example concerns the design of a randomized clinical trial to compare a gel sealant versus standard care for resolving air leaks after pulmonary resection. Read More

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January 2017

Maximum likelihood estimation for stochastic volatility in mean models with heavy-tailed distributions.

Appl Stoch Models Bus Ind 2017 Jul-Aug;33(4):394-408. Epub 2017 Mar 13.

Department of Statistics, Federal University of Rio de Janeiro, Caixa Postal 68530, CEP: 21945-970, Rio de Janeiro, Brazil.

In this article, we introduce a likelihood-based estimation method for the stochastic volatility in mean (SVM) model with scale mixtures of normal (SMN) distributions (Abanto-Valle et al., 2012). Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model with SMN distributions. Read More

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Efficient prediction designs for random fields.

Appl Stoch Models Bus Ind 2015 Mar 26;31(2):178-194. Epub 2014 Nov 26.

Department of Applied Statistics, Johannes-Kepler-University of Linz Linz, Austria.

For estimation and predictions of random fields, it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired by an equivalence theorem type relation to build designs quasi-optimal for the EK variance when space-filling designs become unsuitable. Read More

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