3 edition of **new approach to bilinear time series estimation** found in the catalog.

new approach to bilinear time series estimation

Thorsten Grahn

- 133 Want to read
- 0 Currently reading

Published
**1993**
by Verlag Shaker in Aachen
.

Written in English

- Time-series analysis.

**Edition Notes**

Statement | Thorsten Grahn. |

Series | Reihe Mathematik |

Contributions | Universität Heidelberg. |

Classifications | |
---|---|

LC Classifications | QA280 .G68 1993 |

The Physical Object | |

Pagination | 118 p. : |

Number of Pages | 118 |

ID Numbers | |

Open Library | OL1519090M |

ISBN 10 | 3861114712 |

LC Control Number | 93205303 |

OCLC/WorldCa | 29698653 |

Characteristics of Time Series Threshold models ARCH and GARCH models Bilinear models Nonlinear time series Based on the book by Fan/Yao: Nonlinear Time Series Robert M. Kunst @ University of Vienna and Institute for Advanced Studies Vienna Novem . new approach to real-time bilinear interpolation where input coordinates can appear on smooth curves and lines with non-uniform step size. Section two describes the constraints that such an implementation imposes. The detailed design of the interpolation algorithm and caching system is discussed in section.

The conditions for the invertibility of the model are also included. The estimation of the parameters of the scalar bilinear time series model is considered. The bilinear models are fitted to sunspot numbers and also to a P-wave of a nuclear explosion. The forecasting of sunspot numbers is also considered. Introduction to Time Series Analysis. Lecture Peter Bartlett 1. Review: Time series modelling and forecasting 2. Parameter estimation 3. Maximum likelihood estimator 4. Yule-Walker estimation 5. Yule-Walker estimation: example 1File Size: 57KB.

Abstract: Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN).Cited by: Part of the Advances in Intelligent Systems and Computing book series (AISC, volume ) Abstract This paper considers a class of non-stationary autoregressive systems in which non-stationarity is caused by time varying parameters of the by: 1.

You might also like

Chicken Kiev

Chicken Kiev

Active regions

Active regions

Bird of passage.

Bird of passage.

Classic John Deere two-cylinder tractors

Classic John Deere two-cylinder tractors

The two liturgies, A.D. 1549, and A.D. 1552

The two liturgies, A.D. 1549, and A.D. 1552

The Naesseby Family Archive

The Naesseby Family Archive

The Art of Nature

The Art of Nature

Albert, King of the Belgians

Albert, King of the Belgians

The Accademia seminars

The Accademia seminars

Changes at home.

Changes at home.

return of the Great Britain

return of the Great Britain

Womans rights and ordination.

Womans rights and ordination.

The bilinear BL(p, 0, 1, 1) model is the most general bilinear model which has been shown to be asymptotic normal estimable (in Liu and Chen ()).

Here we propose a new method for estimating the parameter of bilinear time series which is based mainly on a Conditional Least Squares (CLS) approach applied to the AR residuals of the bilinear process. estimate missing values for bilinear time series and specifically, pure bilinear time series.

Bilinear Models. A discrete time series process. is said to be a bilinear time series model of order BL (p, q, P, Q) if it satisfies the difference equation. 1 1 pq Q P t i ti j t j j iti t j t i j ij. x x e bx e e. ϕθ − − −− = = = = = + + +.

Estimation for the first-order diagonal bilinear time series model Article in Journal of Time Series Analysis 11(3) - June with 18 Reads How we measure 'reads'. The paper presents new approach to estimation of the coefficients of an elementary bilinear time series model (EB).

Until now, a lot of authors have considered different identifiability conditions. In the present paper, minimum Hellinger distance estimates for parameters of a bilinear time series model are presented.

The probabilistic properties such as stationarity, existence of moments of the stationary distribution and strong mixing property of the model are well known (see for instance [J. Liu, A note on causality and invertibility of a general bilinear time series model, Adv.

Appl Cited by: Our approach is benefited from the papers by Xie () and Krishnamurthy and Rydén () for, Straumann and Mikosch () for general conditionally heteroscedastic time series, Leroux () for hidden Markov models.

For this purpose define (resp.) the conditional density of given (resp. given) Cited by: 3. In particular, it has been shown that the bilinear model is able to approximate any well-behaved nonlinear relationship to an arbitrary degree of accuracy, in much the same way that an ARMA model provides a good approximation of general linear relationships.1 The bilinear model has been used successfully to model time-series that have been traditionally difficult to fit with classical linear Cited by: As a kind of nonlinear time series models, bilinear models have been widely studied in the literature, see Subba Rao (), Pham and Tran (), Kim et al.

(), Basrak et al. () and Giordano (), among others. It is well known that the general bilinear models are difficult to deal with because of their complex probabilistic by: 5. One of the first models proposed for such purpose in time series analysis is the standard bilinear model proposed by Subba Rao () and Granger and Andersen ().

Bilinear processes B L are a class of non-linear models that has received heightened attention in the probabilistic and statistical literature from their first : M. Maaziz, S. Kharfouchi. Part of the Springer Optimization and Its Applications book series In Section we present the application of a method of adaptive estimation using an algebra-geometric approach, Bilinear Systems and Nonlinear Estimation Theory.

In: Optimization and Control of Bilinear Systems. Springer Optimization and Its Applications, vol 11 Cited by: 1. This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by by: This paper considered application of linear and bilinear time series models in estimating revenue data.

The data. used were monthly revenue, which comprised the allocation from Federal Government and internally generated revenue of a. Local Government Council in Akwa Ibom State. In this chapter, we consider the estimation of a subset bilinear model and we give an algorithm for the estimation of its parameters (see also Gabr and Subba Rao, ).

The method is illustrated with real data. A comparison is then made between the forecasts obtained from the subset bilinear models and other time series by: In most of the literature in time series modeling, generalized autoregressive conditional heterosceasticity (GARCH) models has been used as a traditional model to forecast both the economic and financial time series data.

Though literature has shown that it is not suitable for non-linear time series. For this reason, this model was augmented with bilinear model in order to make it more. The paper presents new approach to estimation of the coefficients of an elementary bilinear time series model (EB).

Until now, a lot of authors have considered different identifiability conditions Author: Daniela Hristova. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open.

The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models.

The aim of this paper is to present a new approach to the filtering problem for the class of bilinear stochastic multivariable systems, consisting in searching for suboptimal state-estimates instead of the conditional statistics.

As a first result, a finite-dimensional optimal linear filter for the considered class of systems is defined. Then, the more general problem of designing polynomial Cited by: Various methods of estimating the parameters of bilinear models are available.

In this paper, the non-linear least squares estimation method is used to estimate the parameters as suggested by Priestley (). The method is recursive in nature and the estimates are obtained when the convergence property is.

a classical time-varying ARMA model. We reason that bilinear time series with time-varying coe±cients may be a useful tool in describing the behavior of a wide class of nonlinear time series. The existence and uniqueness conditions for superdiagonal bilinear models with time varying coe±cients have been discussed by Bibi and Oyet [3].

An Introduction to Bispectral Analysis and Bilinear Time Series Models (Lecture Notes in Statistics) Softcover reprint of the original 1st ed. Edition by T. Subba Rao M. Gabr (Author)Format: Paperback.

On an independent and identically distributed mixture bilinear time-series model Article in Journal of Time Series Analysis 31(2) March with 36 Reads How we measure 'reads'.It retains its original flavor.

It is an applied book with many practical and illustrative examples. It concentrates on the three stages of time series analysis: modeling building, selection, estimation and diagnostic checking and how to iterate the process toward a good solution. The ARIMA time series models are what are by: BILINEAR TIME SERIES 63 where ¹" tº is a sequence of i.i.d.

random variables with mean zero and variance 2 >0. Then the follow-ing statement is given as Theorem 1 in the arXiv version of this article, which also follows from Theorem 1 in Kristensen (). Assume Elnj C b".