Real-time parameter estimation and output prediction for ARMA-type system models
Recursive estimation in autoregressive models with additive outliers
Recursive interpolation of partially observable random fields
Recursive linear estimation for discrete-time systems in the presence of different multiplicative observation noises.
Recursive smoothers for hidden discrete-time Markov chains.
Redescending M-estimators in regression analysis, cluster analysis and image analysis
We give a review on the properties and applications of M-estimators with redescending score function. For regression analysis, some of these redescending M-estimators can attain the maximum breakdown point which is possible in this setup. Moreover, some of them are the solutions of the problem of maximizing the efficiency under bounded influence function when the regression coefficient and the scale parameter are estimated simultaneously. Hence redescending M-estimators satisfy several outlier robustness...
Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms.
Reflection coefficients vs partial autocorrelations.
Regime-switching models of time series with cubic spline transition function in geodetic application
A new class of Smooth Transition Autoregressive models, based on cubic spline type transition functions, has been introduced and subjected to comparison with models based on the traditional logistic transition functions. A very high degree of similarity between the two model classes has been demonstrated. The new class of models can be slightly preferable because of its more simple formal and geometrical structure that may enable users more convenient manipulation in statistical inference procedures....
Regresión espectral sesgada.
Se propone un método de regresión espectral adecuado cuando los regresores tienen potencia espectral despreciable sobre bandas de frecuencia estrechas. Se investiga la relación entre el método propuesto y los procedimientos de regresión sesgada habituales en el dominio del tiempo.
Régression avec résidus spatialement autocorrélés et recherche de la tendance spatiale
Régression trigonométrique et plans d'échantillonnage pour expériences simulées
Régularité d'ordre quelconque pour un modèle statistique filtré
Relative Measurement and Its Generalization in Decision Making. Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors. The Analytic Hierarchy/Network Process.
According to the great mathematician Henri Lebesgue, making direct comparisons of objects with regard to a property is a fundamental mathematical process for deriving measurements. Measuring objects by using a known scale first then comparing the measurements works well for properties for which scales of measurement exist. The theme of this paper is that direct comparisons are necessary to establish measurements for intangible properties that have no scales of measurement. In that case the value...
Remarks on ergodicity and invariant occupation measure in branching diffusions with immigration
Renormalization group of and convergence to the LISDLG process
The LISDLG process denoted by J(t) is defined in Iglói and Terdik [ESAIM: PS7 (2003) 23–86] by a functional limit theorem as the limit of ISDLG processes. This paper gives a more general limit representation of J(t). It is shown that process J(t) has its own renormalization group and that J(t) can be represented as the limit process of the renormalization operator flow applied to the elements of some set of stochastic processes. The latter set consists of IGSDLG processes which are generalizations...
Renormalization group of and convergence to the LISDLG process
The LISDLG process denoted by is defined in Iglói and Terdik [ESAIM: PS 7 (2003) 23–86] by a functional limit theorem as the limit of ISDLG processes. This paper gives a more general limit representation of . It is shown that process has its own renormalization group and that can be represented as the limit process of the renormalization operator flow applied to the elements of some set of stochastic processes. The latter set consists of IGSDLG processes which are generalizations of the ISDLG...
Reversible jump MCMC for two-state multivariate Poisson mixtures
The problem of identifying the source from observations from a Poisson process can be encountered in fault diagnostics systems based on event counters. The identification of the inner state of the system must be made based on observations of counters which entail only information on the total sum of some events from a dual process which has made a transition from an intact to a broken state at some unknown time. Here we demonstrate the general identifiability of this problem in presence of multiple...
Robust Kalman filter and its application in time series analysis
Robust mixing.