A statistical variance components framework for mapping imprinted quantitative trait locus in experimental crosses.
A sufficient condition for admissibility in linear estimation
It was recently shown that all estimators which are locally best in the relative interior of the parameter set, together with their limits constitute a complete class in linear estimation, both unbiased and biased. However, not all these limits are admissible. A sufficient condition for admissibility of a limit was given by the author (1986) for the case of unbiased estimation in a linear model with the natural parameter space. This paper extends this result to the general linear model and to biased...
A sufficient statistic and a nonstandard linearization in nonlinear regression
A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring
In the statistical literature, truncated distributions can be used for modeling real data. Due to error of measurement in truncated continuous data, choosing a crisp trimmed point caucuses a fault inference, so using fuzzy sets to define a threshold pointmay leads us more efficient results with respect to crisp thresholds. Arellano-Valle et al. [2] defined a selection distribution for analysis of truncated data with crisp threshold. In this paper, we define fuzzy multivariate selection distribution...
About the best linear unbiased predictor (BLUP) and associated restrictions. (Sobre la construcción del mejor predictor lineal insesgado (BLUP) y restricciones asociadas.)
ABSTRACT - Models with a Low Nonlinearity.
ABSTRACT - Procedures for Epidemic Alternatives.
Accuracies in the theory of logistic models.
Adaptive estimation in linear regression model. II. Asymptotic normality
Adaptive estimation of a quadratic functional of a density by model selection
We consider the problem of estimating the integral of the square of a density from the observation of a sample. Our method to estimate is based on model selection via some penalized criterion. We prove that our estimator achieves the adaptive rates established by Efroimovich and Low on classes of smooth functions. A key point of the proof is an exponential inequality for -statistics of order 2 due to Houdré and Reynaud.
Adaptive estimation of a quadratic functional of a density by model selection
We consider the problem of estimating the integral of the square of a density f from the observation of a n sample. Our method to estimate is based on model selection via some penalized criterion. We prove that our estimator achieves the adaptive rates established by Efroimovich and Low on classes of smooth functions. A key point of the proof is an exponential inequality for U-statistics of order 2 due to Houdré and Reynaud.
Adaptive hard-thresholding for linear inverse problems
A number of regularization methods for discrete inverse problems consist in considering weighted versions of the usual least square solution. These filter methods are generally restricted to monotonic transformations, e.g. the Tikhonov regularization or the spectral cut-off. However, in several cases, non-monotonic sequences of filters may appear more appropriate. In this paper, we study a hard-thresholding regularization method that extends the spectral cut-off procedure to non-monotonic sequences....
Adaptive maximum-likelihood-like estimation in linear models. II. Asymptotic normality
Adaptive maximum-likelihood-like estimation in linear models. I. Consistency
Adaptive trimmed likelihood estimation in regression
In this paper we derive an asymptotic normality result for an adaptive trimmed likelihood estimator of regression starting from initial high breakdownpoint robust regression estimates. The approach leads to quickly and easily computed robust and efficient estimates for regression. A highlight of the method is that it tends automatically in one algorithm to expose the outliers and give least squares estimates with the outliers removed. The idea is to begin with a rapidly computed consistent robust...
Additional Experiment and Linear Statistical Models
An accuracy of parameter estimates need not be sufficient for their unforeseen utilization. Therefore some additional measurement is necessary in order to attain the required precision. The problem is to express the correction to the original estimates in an explicit form.
Admissibility of Henderson's estimators for three variance components
Admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set.
Admissible invariant estimators in a linear model
Let be observation vector in the usual linear model with expectation and covariance matrix known up to a multiplicative scalar, possibly singular. A linear statistic is called invariant estimator for a parametric function if its MSE depends on only through . It is shown that is admissible invariant for , if and only if, it is a BLUE of in the case when is estimable with zero variance, and it is of the form , where and is an arbitrary BLUE, otherwise. This result is used in...
Affine resolvable incomplete block designs