Results 21 to 30 of about 95,197 (267)

The density of multivariate $M$-estimates [PDF]

open access: yesThe Annals of Statistics, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Almudevar, Anthony   +2 more
openaire   +3 more sources

M-Estimators of Scatter with Eigenvalue Shrinkage [PDF]

open access: yesICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data ...
Palomar, Daniel P.   +3 more
openaire   +4 more sources

REGRESI ROBUST MM-ESTIMATOR UNTUK MEMODELKAN JUMLAH KEMATIAN BALITA DI PROVINSI JAWA TIMUR TAHUN 2017

open access: yesJurnal Matematika UNAND, 2021
Dengan berakhirnya MDGs, PBB kembali membuat agenda pembangunan yaitu SDGs dengan salah satu targetnya yaitu mengakhiri kematian balita yang dapat dicegah, dengan seluruh negara menurunkan Angka Kematian Balita 25 per 1000 kelahiran hidup.
ATIKAH RAHMAH PUTRI   +2 more
doaj   +1 more source

Robust and sparse M-estimation of DOA

open access: yesSignal Processing, 2023
A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss ...
Christoph F. Mecklenbräuker   +3 more
openaire   +3 more sources

Comparisons of the Performances of Estimators of a Bounded Normal Mean Under Squared-Error Loss

open access: yesRevstat Statistical Journal, 2009
This paper is concerned with the estimation under squared-error loss of a normal mean θ based on X ∼ N (θ, 1) when |θ| ≤ m for a known m > 0. Nine estimators are compared, namely the maximum likelihood estimator (mle), three dominators of the mle ...
Yiping Dou , Constance van Eeden
doaj   +1 more source

Frequentist Inference on Traffic Intensity of M/M/1 Queuing System [PDF]

open access: yesOperations Research and Decisions, 2023
When we study any queuing system, the performance measures reflect different features of the system. In the classical M/M/1 queuing system, traffic intensity is perhaps the most important performance measure.
Kaustav Dutta, Amit Choudhury
doaj  

PEMODELAN HARGA SAHAM INDEKS LQ45 MENGGUNAKAN REGRESI LINIER ROBUST M-ESTIMATOR: HUBER DAN BISQUARE

open access: yesBarekeng, 2014
Model ordinary least square (OLS) menjadi tidak efisien dan bias jika terdapat pelanggaran asumsi klasik. Salah satu penyebab terjadinya hal tersebut adalah terdapat observasi-observasi yang bersifat ekstrim, dimana observasi-observasi tersebut dapat ...
Lexy J. Sinay, Mozart W. Talakua
doaj   +1 more source

Adaptive Modulation With CAZAC Preamble-Based Signal-to-Noise-Ratio Estimator in OFDM Cooperative Communication System

open access: yesIEEE Access, 2022
This paper presents an adaptive modulation in a single-input-single-output (SISO)-OFDM-based cooperative system that employs a Constant Amplitude Zero Autocorrelation (CAZAC) preamble-based SNR estimator.
Shahid Manzoor, Noor Shamsiah Othman
doaj   +1 more source

Some Thoughts About the Design of Loss Functions

open access: yesRevstat Statistical Journal, 2007
The choice and design of loss functions is discussed. Particularly when computational methods like cross-validation are applied, there is no need to stick to “standard” loss functions such as the L2-loss (squared loss).
Christian Hennig , Mahmut Kutlukaya
doaj   +1 more source

Asymptotic Normality of M-Estimator in Linear Regression Model with Asymptotically Almost Negatively Associated Errors

open access: yesMathematics, 2023
This paper studies a linear regression model in which the errors are asymptotically almost negatively associated (AANA, in short) random variables. Firstly, the central limit theorem for AANA sequences of random variables is established. Then, we use the
Yu Zhang
doaj   +1 more source

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