Results 81 to 90 of about 91,156 (303)

Behavior of the maximum likelihood in quantum state tomography

open access: yesNew Journal of Physics, 2018
Quantum state tomography on a d -dimensional system demands resources that grow rapidly with d . They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix).
Travis L Scholten, Robin Blume-Kohout
doaj   +1 more source

Local asymptotic normality of truncated empirical processes

open access: yesThe Annals of Statistics, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

Restricted Tweedie stochastic block models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley   +1 more source

Local central limit theorems, the high-order correlations of rejective sampling and logistic likelihood asymptotics

open access: yes, 2005
Let I_1,...,I_n be independent but not necessarily identically distributed Bernoulli random variables, and let X_n=\sum_{j=1}^nI_j. For \nu in a bounded region, a local central limit theorem expansion of P(X_n=EX_n+\nu) is developed to any given degree ...
Arratia, Richard   +2 more
core   +2 more sources

On the Preservation of Local Asymptotic Normality under Information Loss

open access: yesThe Annals of Statistics, 1988
This paper considers a situation where there are unobservable random variables, and where what is actually seen are other variables that are less informative than the unobservable ones. It is shown that if the unobservable random variables satisfy certain conditions, such as the LAN conditions, then the observable random variables will also satisfy the
Cam, Lucien Le, Yang, Grace L.
openaire   +3 more sources

Predicting cervical cancer DNA methylation from genetic data using multivariate CMMP

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Epigenetic modifications link the environment to gene expression and play a crucial role in tumour development. DNA methylation, in particular, is gaining attention in cancer research, including cervical cancer, the focus of this study.
Hang Zhang   +5 more
wiley   +1 more source

THE UNIFORM LOCAL ASYMPTOTIC NORMALITY: AN EMPIRICAL PROCESS THEORY APPROACH

open access: yesBulletin of the Korean Mathematical Society, 2003
Summary: We investigate a uniform local asymptotic normality for likelihood ratio processes based on an independent and identically distributed local asymptotic problem. Our tool is empirical process theory.
Bae, Jongsig, Kim, Sungyeun
openaire   +3 more sources

Local asymptotic minimax risk bounds in a locally asymptotically mixture of normal experiments under asymmetric loss [PDF]

open access: yes, 2006
Published at http://dx.doi.org/10.1214/074921706000000527 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
Bhattacharya, Debasis, Basu, A. K.
openaire   +3 more sources

On subset least squares estimation and prediction in vector autoregressive models with exogenous variables

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne   +2 more
wiley   +1 more source

A partial envelope approach for modelling multivariate spatial‐temporal data

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract In the new era of big data, modelling multivariate spatial‐temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses across different locations and time points.
Reisa Widjaja   +3 more
wiley   +1 more source

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