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Bootstrapping in multiplicative models
Journal of Econometrics, 1989zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Srivastava, M. S., Singh, Balvir
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2003
In this chapter, we consider bootstrap methods for some popular time series models, such as the autoregressive processes, that are driven by iid random variables through a structural equation. As indicated in Chapter 2, for such models, it is often possible to adapt the basic ideas behind bootstrapping a linear regression model with iid error variables
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In this chapter, we consider bootstrap methods for some popular time series models, such as the autoregressive processes, that are driven by iid random variables through a structural equation. As indicated in Chapter 2, for such models, it is often possible to adapt the basic ideas behind bootstrapping a linear regression model with iid error variables
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Bootstrapping Threshold Autoregressive Models
2002Threshold autoregressive (TAR) models have been widely used for periodic time series, as they are nonlinear models relatively simple to handle being linear in different regions of the state space, see e.g. Tong (1990). One of the main problems regards the decision of the selection of the correct order of a TAR model.
OHRVIK J, SCHOIER, GABRIELLA
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1992
In the next both chapters we consider bootstrap of least squares estimates and M - estimates in linear models with nonrandom design and i.i.d. error variables. We show that bootstrap works under weaker conditions than asymptotic normality. This will be done for least squares estimates in this chapter and for M - estimates in the next chapter.
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In the next both chapters we consider bootstrap of least squares estimates and M - estimates in linear models with nonrandom design and i.i.d. error variables. We show that bootstrap works under weaker conditions than asymptotic normality. This will be done for least squares estimates in this chapter and for M - estimates in the next chapter.
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Bootstrapping moving average models
Journal of the Italian Statistical Society, 1992Summary: In recent years, the bootstrap method has been extended to time series analysis where the observations are serially correlated. Contributions have focused on the autoregressive model producing alternative resampling procedures. In contrast, apart from some empirical applications, very little attention has been paid to the possibility of ...
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1968
The idea of bootstrap as a possible mechanism underlying the dynamics of strong interactions arose in the early work of Chew and Mandelstam1 on π-π scattering, over five years ago. Since then, a large number of bootstrap calculations have been made for different kinds of systems by using a variety of approximations.† These have made it plausible that ...
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The idea of bootstrap as a possible mechanism underlying the dynamics of strong interactions arose in the early work of Chew and Mandelstam1 on π-π scattering, over five years ago. Since then, a large number of bootstrap calculations have been made for different kinds of systems by using a variety of approximations.† These have made it plausible that ...
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Bootstrapping neural discriminant model
2000In recent years, neural computing techniques have received increasing attention from statisticians. Statistical techniques are formulated in terms of the principle of the likelihood of neural network models, where the connection weights of the network are treated as unknown parameters in classification problems.
Masaaki Tsujitani, Takashi Koshimizu
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The statistical bootstrap model
1972A review is presented of the statistical bootstrap model of Hagedorn and Frautschi. This model is an attempt to apply the methods of statistical mechanics in high-energy physics, while treating all hadron states (stable or unstable) on an equal footing.
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Determination of Asymptotic Parameters in the Statistical Bootstrap Model
, 1971C. Hamer, S. Frautschi
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Phase transitions in the statistical bootstrap model with an internal symmetry
, 1980K. Redlich, L. Turko
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