Asymptotic normality of the spectral density estimators for almost periodically correlated stochastic processes

نویسندگان

چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

SHIFT OPERATOR FOR PERIODICALLY CORRELATED PROCESSES

The existence of shift for periodically correlated processes and its boundedness are investigated. Spectral criteria for these non-stationary processes to have such shifts are obtained.

متن کامل

Asymptotic Normality for Deconvolving Kernel Density Estimators

Suppose that we have 11 observations from the convolution model Y = X + £, where X and £ are the independent unobservable random variables, and £ is measurement error with a known distribution. We will discuss the asymptotic normality for deconvolving kernel density estimators of the unknown density f x 0 of X by assuming either the tail of the characteristic function of £ behaves as II I~Oexp(...

متن کامل

Spectral Theory for Periodically and Almost Periodically Correlated Random Processes: A Survey

This paper contains a survey of the spectral theory of periodically correlated and almost periodically correlated stochastic processes. These processes are also called cyclostationary and almost cyclostationary, and we give give some remarks concerning the historical development of the two nomenclatures. In this paper we survey the spectral theory of the covariance, the estimation of the Fourie...

متن کامل

ASYMPTOTIC NORMALITY OF STATISTICAL−FUNCTION ESTIMATORS FOR GENERALIZED ALMOST−CYCLOSTATIONARY PROCESSES (ThuAmOR7)

The problem of estimating second−order statistical functions of generalized almost−cyclostationary (GACS) processes is addressed. The class of such nonstationary processes includes, as a special case, the almost−cyclostationary (ACS) processes. ACS processes filtered by Doppler channels and communications signals with time−varying parameters are further examples. It is shown that, for GACS proc...

متن کامل

Asymptotic Normality of Kernel Type Density Estimators for Random Fields

Kernel type density estimators are studied for random fields. It is proved that the estimators are asymptotically normal if the set of locations of observations become more and more dense in an increasing sequence of domains. It turns out that in our setting the covariance structure of the limiting normal distribution can be a combination of those of the continuous parameter and the discrete pa...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Stochastic Processes and their Applications

سال: 1994

ISSN: 0304-4149

DOI: 10.1016/0304-4149(94)90033-7