Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Suhasini Subba Rao"'
Publikováno v:
IEEE Access, Vol 10, Pp 116844-116857 (2022)
Clustering is a challenging problem in machine learning in which one attempts to group $N$ objects into $K_{0}$ groups based on $P$ features measured on each object. In this article, we examine the case where $N \ll P$ and $K_{0}$ is not known. Clust
Externí odkaz:
https://doaj.org/article/ec1c1b68053b46d19f306200afaad963
Autor:
Suhasini Subba Rao, Junho Yang
Publikováno v:
Journal of Time Series Analysis. 44:23-42
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82babca8bd6a5e427471f88c38cdfd81
https://doi.org/10.1111/jtsa.12648
https://doi.org/10.1111/jtsa.12648
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47ce936cf0d3726daa431859210dfd88
https://doi.org/10.2139/ssrn.4037118
https://doi.org/10.2139/ssrn.4037118
The periodogram is a widely used tool to analyze second order stationary time series. An attractive feature of the periodogram is that the expectation of the periodogram is approximately equal to the underlying spectral density of the time series. Ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f68a6648838ccf3fa72aab30db88a0c3
Autor:
Suhasini Subba Rao, Junho Yang
In time series analysis there is an apparent dichotomy between time and frequency domain methods. The aim of this paper is to draw connections between frequency and time domain methods. Our focus will be on reconciling the Gaussian likelihood and the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17b8784d3b1d3ecbff4c20ae8ac215e6
Autor:
Suhasini Subba Rao, Junbum Lee
Publikováno v:
Statistics. 51:949-968
In this paper, general quadratic forms of nonstationary, α-mixing time series are considered. Under mixing and moment assumptions, asymptotically normality of these forms are derived. These results...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d315ea7fbf727e85dc54702c07136ddd
https://doi.org/10.1080/02331888.2017.1318880
https://doi.org/10.1080/02331888.2017.1318880
Publikováno v:
Journal of Time Series Analysis. 38:326-351
Many random phenomena in the environmental and geophysical sciences are functions of both space and time; these are usually called spatio-temporal processes. Typically, the spatio-temporal process is observed over discrete equidistant time and at irr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4598417c6df6d8a5506164f948b16eb8
https://doi.org/10.1111/jtsa.12222
https://doi.org/10.1111/jtsa.12222
Publikováno v:
Journal of the Royal Statistical Society Series B: Statistical Methodology. 79:95-123
Summary The analysis of spatial data is based on a set of assumptions, which in practice need to be checked. A commonly used assumption is that the spatial random field is second-order stationary. In the paper, a test for spatial stationarity for irr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97c0d52cad25a6b62c5f4efca15901f5
https://doi.org/10.1111/rssb.12161
https://doi.org/10.1111/rssb.12161
Autor:
Suhasini Subba Rao
Publikováno v:
Ann. Statist. 46, no. 2 (2018), 469-499
A class of Fourier based statistics for irregular spaced spatial data is introduced. Examples include the Whittle likelihood, a parametric estimator of the covariance function based on the $L_{2}$-contrast function and a simple nonparametric estimato
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0cc39e288f5e6f9b9b492353f9806f4
https://doi.org/10.1214/17-aos1556
https://doi.org/10.1214/17-aos1556
Autor:
Suhasini Subba Rao, Carsten Jentsch
Publikováno v:
Journal of Econometrics. 185:124-161
It is well known that the discrete Fourier transforms (DFT) of a second order stationary time series between two distinct Fourier frequencies are asymptotically uncorrelated. In contrast for a large class of second order nonstationary time series, in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0bdf320f24104ac2140fdd277aa4552
https://doi.org/10.1016/j.jeconom.2014.09.010
https://doi.org/10.1016/j.jeconom.2014.09.010