Statistical Learning With Time Series Dependence: An Application to Scoring Sleep in Mice

ABSTRACT: We review and evaluate the methods commonly used in the accounting literature to correct for cross-sectional and time-series dependence. While much of the accounting literature studies settings in which variables are cross-sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2 statistic and Newey-West corrected Fama-MacBeth standard errors do not correct for both cross-sectional and time-series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the implied cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well-specified test statistics.

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Detection by a permutation test of periodicities in biological time series: Dependence of sensitivity on measurement precision

Annals of Biomedical Engineering ◽

10.1007/bf02363559 ◽

1976 ◽

Vol 4 (1) ◽

pp. 68-77 ◽

Cited By ~ 5

Author(s):

Stuart W. Smith ◽

Robert H. Odell

Keyword(s):

Time Series ◽

Permutation Test ◽

Measurement Precision ◽

Biological Time ◽

Series Dependence

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Study on Prediction of Mackey-Glass Chaotic Time Series Using Support Vector Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1051.1009 ◽

2014 ◽

Vol 1051 ◽

pp. 1009-1015 ◽

Cited By ~ 2

Author(s):

Ya Li Ning ◽

Xin You Wang ◽

Xi Ping He

Keyword(s):

Machine Learning ◽

Time Series ◽

Support Vector Machines ◽

Statistical Learning ◽

Chaotic Time Series ◽

Support Vector ◽

Machine Type ◽

Type Selection ◽

Vector Machines ◽

New Generation

Support Vector Machines (SVM), which is a new generation learning method based on advances in statistical learning theory, is characterized by the use of many standard technologies of machine learning such as maximal margin hyperplane, Mercel kernels and the quadratic programming. Because the best performance is obtained in many currently challenging applications, SVM has sustained wide attention, and has been become the standard tools of machine learning and data mining. But as a developing technology, SVM still have some problems and its applications are limited. In this paper, SVM and its applications in chaotic time series including predicting chaotic time series, focus on comparison in regression type selection, and kernel type selection in the same regression machine type.

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Connected Vehicle Data Time Series Dependence for Machine Learning Model Selection and Specification

10.4271/2021-01-0246 ◽

2021 ◽

Author(s):

Dominique Meroux ◽

Cassandra Telenko ◽

Zhen Jiang

Keyword(s):

Machine Learning ◽

Time Series ◽

Model Selection ◽

Learning Model ◽

Connected Vehicle ◽

Vehicle Data ◽

Machine Learning Model ◽

Series Dependence

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Algorithms Research in the Application of Lorenz Time Series Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.1017 ◽

2011 ◽

Vol 268-270 ◽

pp. 1017-1020

Author(s):

Man Xiang Miao ◽

Yi Jin Gang

Keyword(s):

Time Series ◽

Statistical Learning ◽

Time Series Prediction ◽

Chaotic Time Series ◽

Support Vector ◽

Rbf Networks ◽

Machine Learning Methods ◽

Simulation Results ◽

Time Series Simulation ◽

Better Than

Prediction of Lorenz Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine (SVM) is a kind of novel machine learning methods based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper combined SVM with neural network which based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Lorenz Chaotic Time Series. Simulation results show that the effect is better than other methods.

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