Results 291 to 300 of about 1,540,370 (339)

Penalized Likelihood Functional Regression

Statistica Sinica, 2014
This paper studies the generalized functional linear model with a scalar response and a functional predictor. The response given the functional predictor is assumed to come from the distribution of an exponential family. A penalized likelihood approach is proposed to estimate the unknown intercept and coefficient function in the model.
Pang Du, Xiao Wang
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Classification by likelihood accordance functions

Communications in Statistics - Simulation and Computation, 2021
In this paper, we introduce the likelihood accordance function (LA function for short), which is defined to characterize the accordance of a new observation to be classified with training samples.
Yuqi Long, Xingzhong Xu
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Quadratic Artificial Likelihood Functions Using Estimating Functions

Scandinavian Journal of Statistics, 2006
Abstract.  A vector‐valued estimating function, such as the quasi‐score, is typically not the gradient of any objective function. Consequently, an analogue of the likelihood function cannot be unambiguously defined by integrating the estimating function.
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Assigning a Likelihood Function

2020
As scientists, we want to know how to parameterise our models, make comparisons with other models, and quantify model predictive uncertainty. For all these purposes, measurement data are needed, but how exactly should we use the data? The answer is always the same: in the likelihood function.
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Information from the maximized likelihood function

Biometrika, 1985
Suppose x = (x1, ..., xj) is a random sample of either scalar or vector observations from a density f(x, c(), where w( E Q is partitioned into a set 0 = (01, ..., Or) of parameters of direct interest and 4 = (4 1, ..., 4,q) of nuisance parameters.
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On empirical likelihood statistical functions

Journal of Econometrics, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, G, Yuan, A, Xu, J
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Estimating Functions and Approximate Conditional Likelihood

Biometrika, 1987
The approximate conditional likelihood method proposed by \textit{D. R. Cox} and \textit{N. Reid}, J. R. Stat. Soc., Ser. B 49, 1-39 (1987; Zbl 0616.62006) is applied to the estimation of a scalar parameter \(\theta\), in the presence of nuisance parameters.
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STATISTICAL INFERENCE WITH SIMULATED LIKELIHOOD FUNCTIONS

Econometric Theory, 1999
Summary: This paper considers classical test statistics, namely, the likelihood ratio, efficient score, and Wald statistics, for econometric models under simulation estimation. The simulated likelihood ratio, simulated efficient score, and simulated Wald test statistics are shown to be asymptotically equivalent.
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Inferential estimation, likelihood, and maximum likelihood linear estimation functions

1991
Abstract The purpose of’inferential’ estimation, as contrasted with ‘point’ estimation, is to make estimation statements. An estimation statement is a quantitative statement of uncertainty about μ using all the information supplied by X.
S R Chamberlin, D A Sprott
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Factoring the likelihood function

1996
Abstract The likelihood function provides an overall assessment of the relative merits of different members of a given family of statistical models, although this must be balanced against their relative complexity. However, as we saw in Section 3.6.3, we often require measures of precision of the estimates of individual parameters in the
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