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Generalization error bounds for stationary autoregressive models  [PDF]
Daniel J. McDonald,Cosma Rohilla Shalizi,Mark Schervish
Computer Science , 2011,
Abstract: We derive generalization error bounds for stationary univariate autoregressive (AR) models. We show that imposing stationarity is enough to control the Gaussian complexity without further regularization. This lets us use structural risk minimization for model selection. We demonstrate our methods by predicting interest rate movements.
Dynamic Factor Models, Cointegration, and Error Correction Mechanisms  [PDF]
Matteo Barigozzi,Marco Lippi,Matteo Luciani
Statistics , 2015,
Abstract: The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors $\mathbf F_t$ are $I(1)$ and singular, i.e. $\mathbf F_t$ has dimension $r$ and is driven by a $q$-dimensional white noise, the common shocks, with $q < r$, (2) the idiosyncratic components are $I(1)$. We show that $\mathbf F_t$ is driven by $r-c$ permanent shocks, where $c$ is the cointegration rank of $\mathbf F_t$, and $q-(r-c)
A goodness-of-fit test of the errors in nonlinear autoregressive time series models with stationary $α$-mixing error terms  [PDF]
Kyong-Hui Kim,Myong-Guk Sin,Ok-Kyong Kim
Statistics , 2014,
Abstract: In this work we deal with the problem of fitting an error density to the goodness-of-fit test of the errors in nonlinear autoregressive time series models with stationary $\alpha$-mixing error terms. The test statistic is based on the integrated squared error of the nonparametric error density estimate and the null error density. By deriving the asymptotic normality of test statistics in these models, we extend the result of Cheng and Sun (Statist. Probab. Lett. \textbf{78}, 1(2008), 50-59) in the model with i.i.d error terms to the more general case.
Moderate deviations for the mildly stationary autoregressive models with dependent errors  [PDF]
Hui Jiang,Mingming Yu,Guangyu Yang
Statistics , 2015,
Abstract: In this paper, we consider the normalized least squares estimator of the parameter in a mildly stationary first-order autoregressive model with dependent errors which are modeled as a mildly stationary AR(1) process. By martingale methods, we establish the moderate deviations for the least squares estimators of the regressor and error, which can be applied to understand the near-integrated second order autoregressive processes. As an application, we obtain the moderate deviations for the Durbin-Watson statistic.
Estimation in autoregressive model with measurement error  [PDF]
Jér?me Dedecker,Adeline Samson,Marie-Luce Taupin
Statistics , 2011,
Abstract: Consider an autoregressive model with measurement error: we observe $Z_i=X_i+\epsilon_i$, where $X_i$ is a stationary solution of the equation $X_i=f_{\theta^0}(X_{i-1})+\xi_i$. The regression function $f_{\theta^0}$ is known up to a finite dimensional parameter $\theta^0$. The distributions of $X_0$ and $\xi_1$ are unknown whereas the distribution of $\epsilon_1$ is completely known. We want to estimate the parameter $\theta^0$ by using the observations $Z_0,..,Z_n$. We propose an estimation procedure based on a modified least square criterion involving a weight function $w$, to be suitably chosen. We give upper bounds for the risk of the estimator, which depend on the smoothness of the errors density $f_\epsilon$ and on the smoothness properties of $w f_\theta$.
A Note on Comparison of Error Correction Codes  [PDF]
Dejan V. Djonin
Mathematics , 2007,
Abstract: Use of an error correction code in a given transmission channel can be regarded as the statistical experiment. Therefore, powerful results from the theory of comparison of experiments can be applied to compare the performances of different error correction codes. We present results on the comparison of block error correction codes using the representation of error correction code as a linear experiment. In this case the code comparison is based on the Loewner matrix ordering of respective code matrices. Next, we demonstrate the bit-error rate code performance comparison based on the representation of the codes as dichotomies, in which case the comparison is based on the matrix majorization ordering of their respective equivalent code matrices.
On non-stationary threshold autoregressive models  [PDF]
Weidong Liu,Shiqing Ling,Qi-Man Shao
Statistics , 2011, DOI: 10.3150/10-BEJ306
Abstract: In this paper we study the limiting distributions of the least-squares estimators for the non-stationary first-order threshold autoregressive (TAR(1)) model. It is proved that the limiting behaviors of the TAR(1) process are very different from those of the classical unit root model and the explosive AR(1).
Permutationally Invariant Codes for Quantum Error Correction  [PDF]
Harriet Pollatsek,Mary Beth Ruskai
Physics , 2003,
Abstract: A permutationally invariant n-bit code for quantum error correction can be realized as a subspace stabilized by the non-Abelian group S_n. The code corresponds to bases for the trivial representation, and all other irreducible representations, both those of higher dimension and orthogonal bases for the trivial representation, are available for error correction. A number of new (non-additive) binary codes are obtained, including two new 7-bit codes and a large family of new 9-bit codes. It is shown that the degeneracy arising from permutational symmetry facilitates the correction of certain types of two-bit errors. The correction of two-bit errors of the same type is considered in detail, but is shown not to be compatible with single-bit error correction using 9-bit codes.
Analog quantum error correction  [PDF]
Seth Lloyd,Jean-Jacques E. Slotine
Physics , 1997, DOI: 10.1103/PhysRevLett.80.4088
Abstract: Quantum error-correction routines are developed for continuous quantum variables such as position and momentum. The result of such analog quantum error correction is the construction of composite continuous quantum variables that are largely immune to the effects of noise and decoherence.
Quantum Error Correction Beyond Completely Positive Maps  [PDF]
A. Shabani,D. A. Lidar
Physics , 2006,
Abstract: By introducing an operator sum representation for arbitrary linear maps, we develop a generalized theory of quantum error correction (QEC) that applies to any linear map, in particular maps that are not completely positive (CP). This theory of "linear quantum error correction" is applicable in cases where the standard and restrictive assumption of a factorized initial system-bath state does not apply.
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