regression problem
A Fast Iteration Method for Mixture Regression Problem
... nonlinear regression, nonparame- tric regression, generalized linear ...mixture regression problem discussed in this paper is a situation with mixed ...mixture regression problem ...
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Regression Problem Using Nerul Network to Predict Real-Value Output
... Clustering is the method of classification of objects into different groups. It partitions the data set into subsets or clusters, in such a manner that the data in every subset share some ordinary trait often in ...
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Selecting the Quantity of Models in Mixture Regression
... Mixture regression is a special situation in regression ...mixture regression are from multiple distributions (the information of which distribution every observation from is unknown), which will ...
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Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... Forward Regression (UOFR) algorithm is introduced for nonlinear system identification, which includes converting a least squares regression problem into the associated ultra least squares ...
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Wind turbine Cpmax and drivetrain-losses estimation using Gaussian process machine learning
... The results presented here first demonstrate that GP is superior to LS in this case, with LS results being so scattered as to make them effectively useless. The GP results, while more tightly clustered, do suffer from a ...
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Probability density function estimation using orthogonal forward regression
... a regression problem and it selects sparse kernel density estimates based on an orthogonal forward regression (OFR) algorithm that incrementally minimises the training mean square error ...sparse ...
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A GA optimized SAX ANN based Stock Level Prediction System
... forecasting problem as a regression problem and attempts to forecast one-day ahead stock price trends using a GA optimized symbolic aggregate approximation (SAX)- ANN based regression ...
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Inference in the Presence of Likelihood Monotonicity for Polytomous and Logistic Regression
... multinomial regression problem that features nuisance para- meters estimated at infinity to a similar problem in which all nuisance parameters have finite estimates; this conversion is such that the ...
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A complete investigation of using weighted kernel regression for the case of small sample problem with noise
... A complete extension of WKR is proposed to address regression problem with noisy training samples. The investigation branches into two parts (1) investigation on weight parameters techniques and (2) ...
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Adaptive regression and model selection in data mining problems
... The logistic regression problem can be solved using BMARS algorithm with the linear least squares fit replaced with the procedure performing linear logistic regression procedure which es[r] ...
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Extending the Behrens Fisher Problem to Testing Equality of Slopes in Linear Regression: The Bayesian Approach
... this problem, viewed as one of the most fascinating approaches of statistical in- ference on the means of heterogeneous normal populations, will be the main focus of this paper, and we shall also give special ...
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Statistical methods for elimination of guarantee-time bias in cohort studies: a simulation study
... Cox regression, time- dependent Cox regression and landmark method based on time-fixed Cox ...Cox regression was shown to be vulnerable to guarantee-time bias [20, ...Cox regression approach ...
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Fuzzy Least Squares Linear Regression Approach to Ascertain Stochastic Demand in the Vehicle Routing Problem
... liner regression is a powerful tool for decision making in fuzzy environ- ...Fuzzy regression analysis is a fuzzy (or possibil- ity) type of classical regression ...
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RELATIONSHIP BETWEEN HARDINESS AND METACOGNITIONS WITH WAYS OF COPING WITH STRESS AMONG FEMALE HIGH-SCHOOL STUDENTSLeyla Karimi and Dr. Seyed Ahmad Mirjafari*DOWNLOAD/VIEW
... coefficient Regression has been possible among the dimensions of meta-cognitive beliefs, the dimension of uncontrollability, and the cognitive dimension for a standard deviation, 34% and 28%, respectively, ...
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Stationarity of time series and the problem of spurious regression
... While not being too technical, the goal of this paper was to introduce some general issues of non-stationarity for practitioners, students and beginning researchers. Using standard methodology of data generating ...
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Dealing Heteroscedasticity Problem in Regression Modeling Using ML-Fisher Scoring Algorithm: Simulation Study
... term, the heteroscedasticity problem may lead to bias estimates and strongly influence the inference of the regression parameters. It might produce the misleading conclusion of the hypothesis testing. This ...
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Logistic regression analysis in personal loan bankruptcy
... In this research, Binary Logistic Regression was explored to estimate the parameters. To assess the factors contributed to personal loans bankruptcy, we focuses on analysing the four predictors, that is age, ...
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Learning Comprehensible Theories from Structured Data
... This thesis is concerned with the problem of learning comprehensible theories from structured data and covers primarily classification and regression learning. The basic knowledge representation language is ...
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MULTI LEVEL GROUP KEY MANAGEMENT TECHNIQUE FOR MULTICAST SECURITY IN MANET
... Abnormal play in such top-level competition can be expressed as “Choking” phenomenon too. As a result, this article intends to explain the phenomenon from a psychological point of view and try to manage and take control ...
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Bayes Wavelet Regression Approach to Solve Problems in Multivariable Calibration Modeling
... the problem of ill conditioned and the singularity has been ...Continuum Regression (CR-WT) which resulted in a relatively satisfactory model to solve the problem of ill conditioned and the ...
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