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Transition Matrix Theory And Individual Claim Loss Development
"... Motivation. Individual claim development is important for creating the average severity distributions that underlie most increased limits, and reinsurance pricing analyses, but most current methods do not adequately represent the true process. Method. Transition Matrix Theory is applied to a large d ..."
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Motivation. Individual claim development is important for creating the average severity distributions that underlie most increased limits, and reinsurance pricing analyses, but most current methods do not adequately represent the true process. Method. Transition Matrix Theory is applied to a large
1 STOCHASTIC TRANSITION MATRIX APPROACH TO STOCHASTIC TRANSPORT
"... Abstract. The “Stochastic Transition Matrix approach ” is described, and its implementation is demonstrated through an analytically solvable twostream stochastic transport model. It is shown that the STM approach is easier and computationally less demanding to implement than solving the ModifiedLe ..."
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Abstract. The “Stochastic Transition Matrix approach ” is described, and its implementation is demonstrated through an analytically solvable twostream stochastic transport model. It is shown that the STM approach is easier and computationally less demanding to implement than solving the Modified
Decreasing the Bandwidth of a Transition Matrix
, 1994
"... Adapting the competitions method of Freivalds to the setting of unboundederror probabilistic computation, we prove that, for any ffl 2 (0; 1], BandMatInv(n ffl ) is logspace complete for the class of languages recognized by logspace unboundederror probabilistic Turing machines (PL). This ext ..."
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. This research was supported by the National Science Foundation under Grant No. CDA 8822724. 1 Introduction For an arbitrary function f , such that 8n 2 N , f(n) 2 (0; n], BandMatInv(f) denotes the following problem: for each positive integer n and a diagonally dominant nbyn f(n) banded matrix A whose
Transition Matrix Estimation in High Dimensional Time Series
"... In this paper, we propose a new method in estimating transition matrices of high dimensional vector autoregressive (VAR) models. Here the data are assumed to come from a stationary Gaussian VAR time series. By formulating the problem as a linear program, we provide a new approach to conduct inferenc ..."
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Cited by 5 (4 self)
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inference on such models. In theory, under a doubly asymptotic framework in which both the sample size T and dimensionality d of the time series can increase (with possibly d ≫ T), we provide explicit rates of convergence between the estimator and the population transition matrix under different matrix
Empirical transition matrix of multistate models: the etm package
 J Stat Softw 2011
"... When dealing with complex event history data in which individuals may experience more than one single event type, multistate models provide a relevant modelling framework. Well known examples include the competing risks model in which subjects may die from several possible causes, and the illnessde ..."
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Cited by 10 (2 self)
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death model that permits to study the impact of an intermediate event on a terminal event. Quantities of interest in this framework are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the AalenJohansen estimator. In this talk we present the R
Transition matrix Monte Carlo and flathistogram algorithm
, 2003
"... In any valid Monte Carlo sampling that realizes microcanonical property we can collect statistics for a transition matrix in energy. This matrix is used to determine the density of states, from which most of the thermodynamical averages can be calculated, including free energy and entropy. We discus ..."
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In any valid Monte Carlo sampling that realizes microcanonical property we can collect statistics for a transition matrix in energy. This matrix is used to determine the density of states, from which most of the thermodynamical averages can be calculated, including free energy and entropy. We
Consider the twostate Markov chain with transition matrix
"... where 0 < a < 1, 0 < b < 1 and 0 < a + b < 2. may be displayed (Fig. 1) on a tree diagram for this chain. The branch probabilities Figure 1 The above matrix P has a fixed vector a = (at, 1 at) and limiting matrix A = lim n*oo / at 1 at \ \ oil • 1 ~ a l I for 0 < a ^ < 1. ..."
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. The entries c^1 and (1 o^) maybe interpreted as the limiting proportion of times that the process is in state s1 and s2, as the number of steps, n, increases indefinitely. For example, the Markov chain transition matrix
Rescaled range and transition matrix analysis of DNA sequences
 Comm. Theor. Phys
, 2000
"... In this paper we treat some fractal and statistical features of the DNA sequences. First, a fractal record model of DNA sequence is proposed by mapping DNA sequences to integer sequences, followed by R/S analysis of the model and computation of the Hurst exponents. Second, we consider transition bet ..."
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Cited by 12 (4 self)
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between the four kinds of bases within DNA. The transition matrix analysis of DNA sequences shows that some measures of complexity based on transition proportion matrix are of interest. The main results are: 1) Hexon> H intron for virus. But H intron> Hexon for the species which have the shape
Transitionmatrix model of bioturbation and radionuclide diagenesis
 Limnology and Oceanography
, 2001
"... Abstract Bioturbation rates in muddy sediments are thought to be due primarily to the reworking activities of benthic deposit feeders. However, current mathematical models of bioturbation do not explicitly link rates of particle mixing with realistic biological reworking mechanisms. To address this ..."
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this problem, I present a transitionmatrix model of bioturbation that quantitatively links the reworking activities of individual organisms and communitylevel particlemixing rates. Solutions to the model are presented for two kinds of tracers; particlereactive radionuclides with a constant input flux
Results 11  20
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5,051