A two-stage procedure on comparing several experimental treatments and a control -- the common and unknown variance case.
A Two-Stage Sampling by Unequal Size Samples from Two Normal Populations with Unknown Variances.
A weighted version of Gamma distribution
Weighted Gamma (WG), a weighted version of Gamma distribution, is introduced. The hazard function is increasing or upside-down bathtub depending upon the values of the parameters. This distribution can be obtained as a hidden upper truncation model. The expressions for the moment generating function and the moments are given. The non-linear equations for finding maximum likelihood estimators (MLEs) of parameters are provided and MLEs have been computed through simulations and also for a real data...
About one problem of Bernoulli and Euler from the theory of statistical estimation.
We consider some results by D. Bernoulli and L. Euler on the method of maximum likelihood in parametric estimation. The statistical analysis is made by considering a parametric family with a shift parameter.
ABSTRACT - Models with a Low Nonlinearity.
ABSTRACT - Procedures for Epidemic Alternatives.
ABSTRACT - Some Tests on Exponential Populations.
Accuracies in the theory of logistic models.
Accuracy of Proportion Estimators: a Simple Rule.
Adaptive estimation in linear regression model. II. Asymptotic normality
Adaptive estimation of the transition density of a Markov chain
Adaptive goodness-of-fit testing from indirect observations
In a convolution model, we observe random variables whose distribution is the convolution of some unknown density f and some known noise density g. We assume that g is polynomially smooth. We provide goodness-of-fit testing procedures for the test H0: f=f0, where the alternative H1is expressed with respect to -norm (i.e. has the form ). Our procedure is adaptive with respect to the unknown smoothness parameterτ of f. Different testing rates (ψn) are obtained according to whether f0 is polynomially...
Adaptive maximum-likelihood-like estimation in linear models. I. Consistency
Adaptive maximum-likelihood-like estimation in linear models. II. Asymptotic normality
Adaptive procedures
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...
Additions and correction to “The bootstrap of the mean with arbitrary bootstrap sample”
Additive Covariance kernels for high-dimensional Gaussian Process modeling
Gaussian Process models are often used for predicting and approximating expensive experiments. However, the number of observations required for building such models may become unrealistic when the input dimension increases. In oder to avoid the curse of dimensionality, a popular approach in multivariate smoothing is to make simplifying assumptions like additivity. The ambition of the present work is to give an insight into a family of covariance kernels that allows combining the features of Gaussian...
Addressing the problem of lack of representativeness on syndromic surveillance schemes
A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means. This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence of disease,...
Admissibility of Generalized Bayes Estimators in An One Parameter Nonregular Family.