Regression problems
The variational Bayes method for inverse regression problems with an application to the palaeoclimate reconstruction
... The palaeoclimate reconstruction problem is described as an example of inverse regression problems. In the reconstruction problem, past climate is inferred using pollen data. Modern data is used to build a ...
21
Active learning methods for classification and regression problems
... regression, the method based on the pool of regressors and two additional strategies based on the selection of the samples distant from the current support vectors are ...for regression problems in ...
141
Interval type 2 A intuitionistic fuzzy logic for regression problems
... Next, we present our experimental analysis and discus- sion of simulation results. We demonstrate the effectiveness of IT2AIFLS on some regression problems. The IT2AIFLS utilises the IT2AIFS [44] which is ...
13
Economic classification and regression problems and neural networks
... technology. Problems solved have been mostly the classification or regression ...Many problems in the field of economics can also be regarded in the terms of classification or regression and ...
8
ARDL model as a remedy for spurious regression: problems, performance and prospectus
... cointegrating regression when money is measured with a stationary measurement error but have a spurious regression when money is measured with a nonstationary measurement error ...
31
Sequential models for time-evolving regression problems with an application to energy demand prediction
... Statistical modelling is being used in an ever increasing range of applications from molecular human biology to the search for exoplanets. These new areas typically pro- vide richer datasets which need correspondingly ...
16
Local Boosting of Decision Stumps for Regression and Classification Problems
... classification problems, each node in a decision stump represents a feature in an instance to be classified, and each branch represents a value that the node can ...In regression problems, DS (or ...
8
Benefitting from the Variables that Variable Selection Discards
... synthetic problems (two regression problems and one classification problem) where performance improves if some variables discarded by variable selection are used as extra ...real problems (DNA ...
20
A combination of genetic algorithm based fuzzy c means with a convex hull based regression for real time fuzzy switching regression analysis: application to industrial intelligent data analysis
... we have demonstrated that the convex hull-based fuzzy regression approach performs efficiently for real-time data processing for fuzzy switching regression problems; this method could re[r] ...
12
Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation
... We describe a Bayesian method for group feature selection in linear regression problems. The method is based on a generalized version of the standard spike-and-slab prior distribution which is often used ...
55
Nonlinear regression without i.i.d. assumption
... nonlinear regression problems without the assumption of being independent and identically ...nonlinear regression and give a numerical ...in regression and machine learning problems, ...
15
Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA
... for regression problems in year is called. support vector regression (SVR) (Vapnic, 1995) Application of support vector regression (SVR) could improve forecasting accuracy ...
27
Regularized Discriminant Analysis, Ridge Regression and Beyond
... In this paper we have provided a solution to an open problem concerning the relationship between multi-class discriminant analysis problems and multivariate regression problems, both in the linear ...
30
Measles, mumps, and rubella vaccination and bowel problems or developmental regression in children with autism: Population study
... of regression and first parental concern with two earlier clinical samples of children, one studied before and the other after the introduction of MMR vaccine (but with no record in those two samples as to whether ...
5
Haßler, Birgit (2009): Global patterns in halogen-induced changes in vertically resolved stratospheric ozone. Dissertation, LMU München: Fakultät für Physik
... then regression analyses found their way into the scientific community, mostly for analyses of total column ozone (from ground-based measurements and satellites) since the temporal quality of the available ...
175
Dynamic Integration of Regression Models
... linear regression is an eager model which tries to approximate the true function by a global linear function and k-nearest neighbours is a lazy model which tries to approximate the true function ...
10
Adaptive Bayesian Function Estimation.
... type II error probabilities are going to zero exponentially fast. Secondly, the prior should have a positive probability in any neighborhood of the true function in a Kullback-Leibler (KL) diver- gence sense. Here the KL ...
156
Lessons learned of emerging Chikungunya virus in two populations of social vulnerability of the Colombian tropics: epidemiological analysis
... The pre-selected variables were integrated into a logistic regression model (Table 6). Different interactions by step- wise selection methods were evaluated until obtaining a sta- tistically consolidated model to ...
10
Are oral health and fixed orthodontic appliances associated with sports injuries and postural stability in elite junior male soccer players?
... Several limitations of this study deserve consider- ation. First, the reliability of self-reports on injuries may be restricted due to response bias (i.e. denial of vulnerability), careless response and lack of insight. ...
7
5445.pdf
... The GPT prior does not have the kernel mixture form (1.1). There will be no clustering of subjects or label switching issues. Instead, the prior f ∼ Π is induced through adding a Gaussian residual to a Gaussian process ...
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