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BOOTSTRAPPING THE GENERAL LINEAR HYPOTHESIS TEST
"... Abstract _ We discuss the use of bootstrap methodology in hypothesis testing, focusing on the classical Ftest for linear hypotheses in the linear model. A modification of the Fstatistics which allows for resampling under the null hypothesis is proposed. This approach is specifically considered in ..."
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Abstract _ We discuss the use of bootstrap methodology in hypothesis testing, focusing on the classical Ftest for linear hypotheses in the linear model. A modification of the Fstatistics which allows for resampling under the null hypothesis is proposed. This approach is specifically considered
Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm
 Machine Learning
, 1988
"... learning Boolean functions, linearthreshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each ex ..."
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Cited by 773 (5 self)
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learning Boolean functions, linearthreshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each
Statistical Analysis of Cointegrated Vectors
 Journal of Economic Dynamics and Control
, 1988
"... We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimen ..."
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Cited by 2749 (12 self)
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We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number
Testing A Linear Hypothesis Using Haar Transform
, 1997
"... . The paper is concerned with the problem of testing a linear hypothesis about regression function. New testing procedure based on the Haar transform is proposed which is adaptive to unknown smoothness properties of the underlying function. The results show that under mild conditions on the design a ..."
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Cited by 3 (0 self)
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. The paper is concerned with the problem of testing a linear hypothesis about regression function. New testing procedure based on the Haar transform is proposed which is adaptive to unknown smoothness properties of the underlying function. The results show that under mild conditions on the design
Inference in Linear Time Series Models with Some Unit Roots,”
 Econometrica
, 1990
"... This paper considers estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots. Our motivating example is a vector autoregression with some unit roots in the companion matrix, which might include polynomials in time as regressors. In the genera ..."
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Cited by 390 (14 self)
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This paper considers estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots. Our motivating example is a vector autoregression with some unit roots in the companion matrix, which might include polynomials in time as regressors
Greedy layerwise training of deep networks
, 2006
"... Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multilayer neural networks have many levels of nonlinearities allow ..."
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Cited by 394 (48 self)
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Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multilayer neural networks have many levels of nonlinearities
Asymptotic expansions of the null distributions of test statistics for multivariate linear hypothesis
, 2001
"... under nonnormality ..."
Determining the Most Significant Parametric Function for a Given Linear Hypothesis
"... After a hypothesis about some linear statistical model has been tested and rejected (e.g., in an AN OVA), many researchers employ the Scheffe procedure to locate the source(s) of the rejection. This procedure guarantees that there is at least one linear combination of the model parameters (consiste ..."
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Cited by 1 (0 self)
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After a hypothesis about some linear statistical model has been tested and rejected (e.g., in an AN OVA), many researchers employ the Scheffe procedure to locate the source(s) of the rejection. This procedure guarantees that there is at least one linear combination of the model parameters
A new method for nonparametric multivariate analysis of variance in ecology.
 Austral Ecology,
, 2001
"... Abstract Hypothesistesting methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, ..."
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Cited by 368 (4 self)
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Abstract Hypothesistesting methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues
Mathematical Applications; Mathematical [models; Matrices; *Statittical'Analysts IDENTIFIERS *Multivariate General Linear Hypothesis 4',
"... ABSTRACT e The multivariate general linear hypothesis (MGLH) received relatively little utilization in educational research.and evaluation. This is surprising in view of the fat,thakredent publiq'tFons have made the MGLH tractable by practitionekrs. This paper seeks to stimulate interest in the ..."
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ABSTRACT e The multivariate general linear hypothesis (MGLH) received relatively little utilization in educational research.and evaluation. This is surprising in view of the fat,thakredent publiq'tFons have made the MGLH tractable by practitionekrs. This paper seeks to stimulate interest
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