Effect of Dropout layer on Classical Regression Problems

2020 ◽  
Author(s):  
Atilla Ozgur ◽  
Fatih Nar
Keyword(s):  
Regression Problems ◽  
Classical Regression

Info-Metrics and Statistical Inference

2017 ◽  
Author(s):  
Amos Golan
Keyword(s):  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Regression Problem ◽  
Information Theoretic ◽  
Regression Problems ◽  
The Common ◽  
Benefits And Costs ◽  
Classical Regression

In this chapter I concentrate on continuous inferential problems: problems where the dependent variable is continuous, such as classical regression problems. As in the previous chapter, using duality theory, I show that the info-metrics framework is general enough to include the class of information-theoretic methods as a special case. The formulation is developed for the classical regression problem, but the results apply to many other problems. A detailed discussion of the benefits and costs of using the info-metrics framework is provided and contrasted with other approaches. I use theoretical examples and policy-relevant applications to demonstrate the method. The common problem of misspecification is also discussed and studied within the info-metrics framework. I show that a misspecified model and a correctly specified one can yield similar answers. The appendices provide detailed discussions of the generalized method of moments and the Bayesian method of moments. Both are connected to info-metrics.

scholarly journals Undergraduate Econometrics Instruction: Through Our Classes, Darkly

2017 ◽  
Vol 31 (2) ◽  
pp. 125-144 ◽  
Author(s):  
Joshua D. Angrist ◽  
Jörn-Steffen Pischke
Keyword(s):  
Paradigm Shift ◽  
Causal Effects ◽  
Economic Research ◽  
Back Seat ◽  
The Past ◽  
Past Half Century ◽  
1960S And 1970S ◽  
The 1960S ◽  
Past Half ◽  
Classical Regression

The past half-century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to “explain” economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for “true models” and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

scholarly journals Boosted Mixture of Experts: An Ensemble Learning Scheme

1999 ◽  
Vol 11 (2) ◽  
pp. 483-497 ◽  
Author(s):  
Ran Avnimelech ◽  
Nathan Intrator
Keyword(s):  
Recognition Task ◽  
Mixture Of Experts ◽  
Data Set ◽  
Combination Model ◽  
Digit Recognition ◽  
Learning Procedure ◽  
Nist Database ◽  
Boosting Algorithm ◽  
Regression Problems ◽  
Classification And Regression

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches.

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