Results 1 to 10 of about 118,514 (262)

Generalized coarsened confounding for causal effects: a large-sample framework [PDF]

Journal of Causal Inference
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding.
Ghosh Debashis, Wang Lei
doaj   +2 more sources

Improved the bias in kernel quantile function estimation

AIMS Mathematics, 2023
In this paper, a new estimator for kernel quantile estimation is given to reduce the bias. The asymptotic properties of the proposed estimator was established and it turned out that the bias has been reduced to the fourth power of the bandwidth, while ...
Abdallah Sayah, Nassima Almi
doaj   +1 more source

Estimating Cumulative Distribution Function Using Gamma Kernel [PDF]

Journal of Sciences, Islamic Republic of Iran, 2022
In this article, we propose the gamma kernel estimator for the cumulative distribution functions with nonnegative support. We derive the asymptotic bias and variance of the proposed estimator in both boundary and interior regions and show that it is free
Behzad Mansouri, Sami Atiyah Sayyid Al-Farttosi, Habiballah Mombeni, Rahim Chinipardaz   +3 more
doaj   +1 more source

Fluctuations, bias, variance and ensemble of learners: exact asymptotics for convex losses in high-dimension ^*

Journal of Statistical Mechanics: Theory and Experiment, 2023
Abstract From the sampling of data to the initialisation of parameters, randomness is ubiquitous in modern Machine Learning practice. Understanding the statistical fluctuations engendered by the different sources of randomness in prediction is therefore key to understanding robust generalisation.
Loureiro B., Gerbelot C., Refinetti M., Sicuro G., Krzakala F.   +4 more
openaire   +6 more sources

Bias-Corrected Maximum Likelihood Estimators of the Parameters of the Unit-Weibull Distribution

Austrian Journal of Statistics, 2021
It is well known that the maximum likelihood estimates (MLEs) have appealing statistical properties. Under fairly mild conditions their asymptotic distribution is normal, and no other estimator has a smaller asymptotic variance.
Andre Menezes, Josmar Mazucheli, F. Alqallaf, M. E. Ghitany   +3 more
doaj   +1 more source

Investigating the bias resulted from ignoring bulmer effect on the genetic and economic output in progeny test and genomic selection program [PDF]

Kafkas Universitesi Veteriner Fakültesi Dergisi, 2017
This study aims to investigate the degree of bias resulted from ignoring Bulmer effect during the estimation of genetic and economic progress in progeny test and genomic selection programs.
Reza SEYEDSHARIFI, Sara AZIZYAN, Azadeh BOUSTAN, Jamal SEIFDAVATI, Ali MOJTAHEDIN   +4 more
doaj   +1 more source

Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models

Mathematics, 2022
This work is concerned with multivariate conditional heteroscedastic autoregressive nonlinear (CHARN) models with an unknown conditional mean function, conditional variance matrix function and density function of the distribution of noise.
Joseph Ngatchou-Wandji, Marwa Ltaifa, Didier Alain Njamen Njomen, Jia Shen   +3 more
doaj   +1 more source

Estimating a Finite Population Mean Using Transformed Data in Presence of Random Nonresponse

International Journal of Mathematics and Mathematical Sciences, 2020
Developing finite population estimators of parameters such as mean, variance, and asymptotic mean squared error has been one of the core objectives of sample survey theory and practice.
Nelson Kiprono Bii, Christopher Ouma Onyango, John Odhiambo   +2 more
doaj   +1 more source

Dissipative Deep Neural Dynamical Systems

IEEE Open Journal of Control Systems, 2022
In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks.
Jan Drgona, Aaron Tuor, Soumya Vasisht, Draguna Vrabie   +3 more
doaj   +1 more source

Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference [PDF]

, 2013
This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors.
Fernandez-Val, Ivan, Lee, Joonhwah
core   +2 more sources