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Ramanujan-Fourier series and the conjecture D of Hardy and Littlewood

H. Gopalakrishna Gadiyar, Ramanathan Padma (2014)

Czechoslovak Mathematical Journal

We give a heuristic proof of a conjecture of Hardy and Littlewood concerning the density of prime pairs to which twin primes and Sophie Germain primes are special cases. The method uses the Ramanujan-Fourier series for a modified von Mangoldt function and the Wiener-Khintchine theorem for arithmetical functions. The failing of the heuristic proof is due to the lack of justification of interchange of certain limits. Experimental evidence using computer calculations is provided for the plausibility...

Rate of convergence for a class of RCA estimators

Pavel Vaněček (2006)

Kybernetika

This work deals with Random Coefficient Autoregressive models where the error process is a martingale difference sequence. A class of estimators of unknown parameter is employed. This class was originally proposed by Schick and it covers both least squares estimator and maximum likelihood estimator for instance. Asymptotic behavior of such estimators is explored, especially the rate of convergence to normal distribution is established.

Regime-switching models of time series with cubic spline transition function in geodetic application

Tomáš Bognár, Jozef Komorník, Magda Komorníková (2004)

Kybernetika

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....

Renormalization group of and convergence to the LISDLG process

Endre Iglói (2010)

ESAIM: Probability and Statistics

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

Endre Iglói (2004)

ESAIM: Probability and Statistics

The LISDLG process denoted by J ( t ) 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 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 of the ISDLG...

Robust recursive estimation of GARCH models

Tomáš Cipra, Radek Hendrych (2018)

Kybernetika

The robust recursive algorithm for the parameter estimation and the volatility prediction in GARCH models is suggested. It seems to be useful for various financial time series, in particular for (high-frequency) log returns contaminated by additive outliers. The proposed procedure can be effective in the risk control and regulation when the prediction of volatility is the main concern since it is capable to distinguish and correct outlaid bursts of volatility. This conclusion is demonstrated by...

Robustness of estimation of first-order autoregressive model under contaminated uniform white noise

Karima Nouali (2009)

Discussiones Mathematicae Probability and Statistics

The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.

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