4 edition of **A linear filtering theory approach to recursive credibility estimation** found in the catalog.

A linear filtering theory approach to recursive credibility estimation

Benjamin Zehnwirth

- 154 Want to read
- 29 Currently reading

Published
**1983**
by Macquarie University, School of Economic and Financial Studies in [North Ryde, Australia]
.

Written in English

- System analysis.,
- Estimation theory.,
- Kalman filtering.

**Edition Notes**

Statement | Ben Zehnwirth. |

Series | Research paper ;, no. 274, Research paper (Macquarie University. School of Economic and Financial Studies) ;, no. 274. |

Classifications | |
---|---|

LC Classifications | QA402 .Z435 1983 |

The Physical Object | |

Pagination | 27 p. : |

Number of Pages | 27 |

ID Numbers | |

Open Library | OL2572847M |

ISBN 10 | 0858375230 |

LC Control Number | 85117269 |

OCLC/WorldCa | 12263086 |

Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement. Recursive estimation of the observation and process noise covariances in online Kalman filtering. 8 Applications of Linear Theory. Fundamentals of orbit determination. Chapter A Scattering Theory Approach. Being intended for a graduate-level course, the book assumes familiarity with basic concepts from matrix theory, linear algebra, linear system theory, and random processes. Four appendices at the end of the book provide the reader with background material in all these s: 7.

"Multivariate Latent Risk: A Credibility Approach," ASTIN Bulletin, Cambridge University Press, vol. 38(1), pages , May. Zehnwirth, Ben, "Linear Filtering and Recursive Credibility Estimation," ASTIN Bulletin, Cambridge University Press, vol. 15(1), pages , April. Full references (including those not matched with items on IDEAS). linear regression problem in a recursive manner. The transition to Bayesian ﬁltering and smoothing theory is explained by extending and generalizing the problem. The ﬁrst Kalman ﬁlter of the book is also encountered in this chapter. The Bayesian ﬁltering theory starts in Chapter 4 where we derive the.

Linear Filtering and Recursive Credibility Estimation 19 R NORBERG Unbayesed Credibility Revisited 37 Corrigendum 44 P. EMBRECHTS, M, MAEJIMA and J. TEUGELS Asymptotic Behaviour of Compound Distributions 45 L. CENTENO On Combining Quota-Share and Excess of Loss 49 Obituary 64 Editorial 66 Book Reviews 67 I IETO LTD. A neural filter is a neural network that is synthesized with simulated data (if models of the signal and measurement processes are available) or experimental data (if not) to perform such recursive processing. No assumptions such as linear dynamics, Gaussian distribution, additive noise, and Markov property are required. A properly trained.

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LINEAR FILTERING AND RECURSIVE CREDIBILITY ESTIMATION 23 ¥. Write, A = (a,j), an m x n matrix. The optimal linear estimator minimizes Y, a,jXj I -I J -I over all matrices A of dimension m × n.

THEOREM (Luenberger). If AX is the optimal linear estimator of Y then,i =(Y, X>llxll. Recursive credibility estimation is discussed from the viewpoint of linear filtering theory. A conjunction of geometric mterpretation and the innovation approach leads to general algorithms not developed before.

Moreover, covariance charac-terizations considered by other researchers drop our elegantly as a result of geometric onsiderations. Recursive Causal Linear Filtering for Two-Dimensional Random Fields EUGENE WONG, FELLOW, IEEE Abstract-The causal estimation of a two-parameter Gaussian random field in the presence of an additive, independent, white Gaussian noise is studied.

The dynamics of this random field are. Recursive Update Filtering for Nonlinear Estimation Renato Zanetti Abstract—Nonlinear ﬁlters are often very computationally expensive and usually not suitable for real-time applications.

Real-time navigation algorithms are typically based on linear estimators, such as. Abstract: The inverse and forward dynamics problems for multilink serial manipulators are solved by using recursive techniques from linear filtering and smoothing theory.

The pivotal step is to cast the system dynamics and kinematics as a two-point boundary-value problem. Solution of this problem leads to filtering and smoothing techniques similar to the equations of Kalman filtering and.

Linear filtering is one of the most powerful image enhancement methods. It is a process in which part of the signal frequency spectrum is modified by the transfer function of the filter.

In general, the filters under consideration are linear and shift-invariant, and thus, the output images are characterized by the convolution sum between the input image and the filter impulse response; that is.

The above theorem was ﬁrst given by Kalman in his famous paper, “A new approach to linear ﬁltering and prediction problem”, J. Basic Engineering, ASME, 82 (March A linear filtering theory approach to recursive credibility estimation book, Equation () is especially important in control and estimation theory and is referred to as the discrete time Riccati diﬀerence equation.

The ”ﬁltered. Recursive Models of Dynamic Linear Economies Lars Hansen University of Chicago Doubling algorithm. Concepts of Linear Control Theory. Symplectic Matrices.

Alternative forms of Riccati equation. viii Contents Part II: Representations and Properties 9. Representation and Estimation The Kalman Filter. Innovations. words, linear systems will be specified by systems of first-order difference (or differential) equations. This point of view is A New Approach to Linear Filtering and Prediction Problems1 The classical filtering and prediction problem is re-examined using the Bode-Shannon representation of random processes and the “state transition” method of.

Recursive Credibility Estimation. A New Approach To Linear Filtering and Prediction Problems. It is shown how credibility theory can be applied to solve some realistic insurance problems. (ii) linear filtering regarded as orthogonal projection in Hilbert space [15, pp.

As an important by-product, this approach yielded the Duality Principle [11, 16] which provides a link between (stochastic) filtering theory and (deterministic) control theory. Because of the duality, results on the optimal design of linear control systems.

Kalman filter. Introduction and summary Credibility theory, a cornerstone of actuarial mathematics, can be viewed as a set of Bayesian insurance models.

The credibility estimators are linear Bayes rules and may be derived using a variety of arguments including minimum linear mean-square estimation theory (Luenberger (, Ch. 4)) and Gauss. Adaptive Filter Features Adaptive ﬁlters are composed of three basic modules: Filtering strucure Determines the output of the ﬁlter given its input samples Its weights are periodically adjusted by the adaptive algorithm Can be linear or nonlinear, depending on the application Linear ﬁlters can be FIR or IIR Performance criterion Deﬁned according to application and mathematical tractability.

Recursive Estimation and the Kalman Filter The concept of least-squares regression originates with two people. It is nowadays accepted that Legendre ({) was responsible for the ﬂrst pub-lished account of the theory in ; and it was he who coined the term Moindes Carres or least squares [6].

However, it was Gauss ({) who. Originally published inHarry Van Trees’s Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. Highly readable and practically organized, it is as imperative today for professionals, researchers, and students in optimum signal processing as it was over thirty years ago.

A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system.

The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state.

Exponential Smoothing Claim Amount Credibility Theory Parameter Estimation Procedure Credibility Estimator These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

For a nonlinear filtering problem, the most heuristic and easiest approximation is to use the Taylor series expansion and apply the conventional linear recursive Kalman filter algorithm directly to the linearized nonlinear measurement and transition equations.

First, it is. The Kalman filter is an efficient recursive filter that estimates the internal state of a linear dynamic system from a series of noisy measurements.

It is used in a wide range of engineering and econometric applications from radar and computer vision to estimation of structural macroeconomic models, [17] [18] and is an important topic in. estimation”. In this article, we focus on the problem of how to handle model nonlinearity (concerning both the system model and the mea-surement model) in recursive estimation.

We will review an important variant of the KF, i.e. the unscented Kalman ﬁlter (UKF), which is dedicated to nonlinear estimation. The modern credibility theory is believed to be attributed to the remarkable contribution by Bühlmann (), which is the first one that based the credibility theory on modern Bayes statistics.estimation algorithms via a combination of the standard probability theory and ordinary differential equation approach is discussed in chapter two and then applied to finding the zero ISBN or extremum of a regression function.

In chapter 3 probability theory techniques are used to show.A linear filtering theory approach to recursive credibility estimation / Ben Zehnwirth; Etude et analyse des modeles ARMA de Box-Jenkins en vue de leur utilisation en econometrie / par Rolando Improved productivity of the french fry industry in Victoria / Rene de Jong; Islamic law and the finance of international trade / Alice de Jonge.