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Efficient Stepwise Selection in Decomposable Models
[article]
2013
arXiv
pre-print
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In this paper, we present an efficient way of performing stepwise selection in the class of decomposable models. ...
We also discuss how backward selection can be performed efficiently using our data structures.We also analyze the complexity of the complete stepwise selection procedure, including the complexity of choosing ...
Backward selection procedures for decomposable models are well known in the literature [Wer76, Lau96] , but efficient forward selection procedures have not yet been devel oped. ...
arXiv:1301.2267v1
fatcat:ftak6lcjkbdj5ceqfh5f2h2vza
Estimation of GRACE water storage components by temporal decomposition
2017
Journal of Hydrology
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Results show a clear improvement in using decomposed GRACE data instead of raw GRACE data when compared against total water storage outputs from the AWRA model. ...
The Gravity Recovery and Climate Experiment (GRACE) has been in operation since 2002. ...
Acknowledgements The authors would like to acknowledge those who have been of assistance in producing this work. ...
doi:10.1016/j.jhydrol.2017.06.016
fatcat:hozccdvjdredpdgtvnluryx62i
Chinese companies distress prediction: an application of data envelopment analysis
2014
Journal of the Operational Research Society
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In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical ...
We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. ...
10 Model 11 Model 12 Model 13Model 10: All variables selected by forward stepwise routine.Model 11, 12 and 13: Efficiency variables forced entry, financial variables from Model 10. ...
doi:10.1057/jors.2013.67
fatcat:p3arbnlahfgndjahzmmhnhhneq
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
1994
Journal of the American Statistical Association
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We consider two classes of models that arise in expert systems: the recursive causal models and the decomposable log-linear models. ...
The approach most used currently is a stepwise strategy guided by tests based on approxi. mate asymptotic P-values leading to the selection of a single model; inference is then conditional on the selected ...
Currently, tihe most used approach to model selection in contingency tables is a stepwise one, adapted from stepwise regression by Goodman (1971) ; see also Bishop, Fienberg and Holland (1975, Section ...
doi:10.1080/01621459.1994.10476894
fatcat:agqfnps43rdbraqj7saqhgo76y
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
1994
Journal of the American Statistical Association
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We consider two classes of models that arise in expert systems: the recursive causal models and the decomposable log-linear models. ...
The approach most used currently is a stepwise strategy guided by tests based on approxi. mate asymptotic P-values leading to the selection of a single model; inference is then conditional on the selected ...
Currently, tihe most used approach to model selection in contingency tables is a stepwise one, adapted from stepwise regression by Goodman (1971) ; see also Bishop, Fienberg and Holland (1975, Section ...
doi:10.2307/2291017
fatcat:rkuycucd4ffq3ndp6ui7hrpt3m
Page 3149 of Mathematical Reviews Vol. , Issue 88f
[page]
1988
Mathematical Reviews
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From the summary: “An efficient procedure for model selection from large families of models is described. It is closely related to the all-possible-models approach but is considerably faster. ...
Motivated by the problem of interaction in ANOVA the author pro- poses a stepwise rather than a simultaneous method of fitting the model Y; = f(x;)+¢8;, i= 1,---,.N, where f is a purely periodic in- teraction ...
Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm
2011
Applied Soft Computing
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Second, the RNN, which has a simple architecture and uses numerous fundamental and technical indicators, is applied to construct the input features chosen via Stepwise Regression-Correlation Selection ...
First, the wavelet transform using the Haar wavelet is applied to decompose the stock price time series and thus eliminate noise. ...
One model that may be more efficient than others in stock prediction is the artificial neural network (ANN) [8] . ...
doi:10.1016/j.asoc.2010.09.007
fatcat:guzirywcvfezljlq4gyoucn5ka
An efficient unified model for genome-wide association studies and genomic selection
2017
Genetics Selection Evolution
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Methods: Here, we propose a stepwise linear regression mixed model (StepLMM) to unify GWAS and GS in a single statistical model. ...
Then, in the SNP selection step, the linear mixed model (LMM) for GWAS is equivalently transformed into a simple linear regression to improve computation speed, and the most significant SNP is selected ...
As an alternative, we proposed a stepwise linear mixed regression model that is stable, flexible and computationally efficient. ...
doi:10.1186/s12711-017-0338-x
pmid:28836943
pmcid:PMC5569572
fatcat:ynoqh74b2ndj3iuvqdswxzf66m
Database Design for Quality
[chapter]
1996
Achieving Quality in Software
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The approach employed to define a formal design methodology for the development of easy to use, flexible, efficient and correct database systems is described. ...
Furthermore, it selects a special type of correct transformation, i.e. the stepwise refinement. AM is a mathematical model based on first order logic and a subset of set theory. ...
Demonstration 5: The stepwise refinement has been decomposed in two phases corresponding respectively to data refinement and behavioral refinement since the transformation is a complex task which can be ...
doi:10.1007/978-0-387-34869-8_20
fatcat:oev2uaihvbfhrb6kz2wduykgtu
Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
[article]
2023
arXiv
pre-print
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In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. ...
Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with ...
The authors would like to thank hydrographic office of Fuyang city and Chaohu Research Institute in China especially Juan Tian for her helpful discussions and supply of rich hydrological data. ...
arXiv:2309.10658v1
fatcat:7tfjoh2kfbcfjhpliyonqpeq4y
Spectral Learning Algorithms for Natural Language Processing
2013
North American Chapter of the Association for Computational Linguistics
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His current set of hammers revolve around fast matrix methods (which decompose 2nd moments) and tensor methods for decomposing 3rd moments. ...
His current research interests are machine learning, stepwise regression and computational linguistics. He has been searching for new methods of finding useful features in big data sets. ...
dblp:conf/naacl/CohenCFSU13
fatcat:k2vug2ewdncrxlzqfyhbnr7w3e
Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition
2023
Hydrology
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In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. ...
The forecasting model is obtained through stepwise regression on these components. ...
Stepwise Regression Analysis Stepwise regression, which is a multiple linear regression technique, is an efficient method of selecting the most useful explanatory variables. ...
doi:10.3390/hydrology10020047
fatcat:tkxtv3chirholl4tw44hpocjg4
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
[article]
2024
arXiv
pre-print
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Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback remains a significant ...
To address this challenge, in this paper, we propose a novel self-supervised framework AutoPRM that efficiently enhances the fine-tuning of LLMs for intricate reasoning challenges. ...
Our key insight is that automating stepwise question decomposition provides a natural perspective to reduce problem dimensions, through which model inference and optimization can be more precise and efficient ...
arXiv:2402.11452v1
fatcat:img4n6wfi5bkzhaxluppjccrfi
L0-Regularized Parametric Non-negative Factorization for Analyzing Composite Signals
2011
2011 10th International Conference on Machine Learning and Applications and Workshops
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Since so regularized least squares is NPhard, we propose a stepwise forward/backward optimization to efficiently solve it in an approximated manner. ...
Based on prior physical knowledge about the target, the factors can be modeled as parametric functions, and their parameter values benefit further analyses. ...
Then, both stepwise forward selection and backward pruning are applied to w nnls for finding the optimum weight that minimizes J in (3). ...
doi:10.1109/icmla.2011.84
dblp:conf/icmla/KobayashiWO11
fatcat:mkhvi2bp7bchvjyqsflipituwm
Vulnerability assessment of southern coastal areas of Iran to sea level rise: evaluation of climate change impact
2013
Oceanologia
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Among the different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves are selected for predicting sea level ...
rise by using stepwise regression. ...
In stepwise regression, backward elimination was performed after every forward selection step to remove redundant variables from the model. ...
doi:10.5697/oc.55-3.611
fatcat:vs75y3zbr5adzldsn2tcgko3ra
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