Results 101 to 110 of about 936 (136)

Geometrical Theory of Asymptotic Ancillarity and Conditional Inference

Biometrika, 1982
SUMMARY Differential geometry is applied to the problems of defining higher-order asymptotic ancillarity and of obtaining the asymptotic conditional distribution of an efficient estimator in multiparameter curved exponential families. It is shown that a fundamental role is played in the asymptotic theory of estimation by a one-parameter family of ...
openaire   +2 more sources

MIXED NORMALITY AND ANCILLARITY IN I(2) SYSTEMS

Econometric Theory, 2000
This paper studies asymptotic likelihood inference on cointegration parameters in systems integrated of order two. We start with so-called triangular systems and then extend the analysis to vector autoregressions. We show that even when all unit root restrictions have been imposed, the asymptotic observed information is not (locally) ancillary ...
openaire   +4 more sources

Sufficiency, ancillarity and independence in invariant models

Journal of Statistical Planning and Inference, 1990
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Ancillarity and optimal conditional tests

2013
n testing statistical hypotheses, the ancillarity property can be used to obtain optimal tests when, without conditioning, optimal tests do not exist (Lehmann, 1986). Anyway, two counterexamples show that purpose is not always attainable. In this paper only parametric models for experiments will be considered.
openaire   +2 more sources

On partial sufficiency and partial ancillarity

Scandinavian Actuarial Journal, 1967
Abstract In connection with a new model for two-way sample schemes with discrete observations introduced by Rasch [11] and [12], the idea of basing the statistical analysis entirely upon conditional distributions was suggested. The main feature of this method of analysis can be summarized as follows.
openaire   +1 more source

An Ancillarity Paradox in the Estimation of Multinomial Probabilities

Journal of the American Statistical Association, 1990
Abstract Let X be a multinomial (n, p) variable, where n is an ancillary statistic. In Section 2, it is shown that the minimax estimator of p for fixed sample size n is not minimax for squared error loss. In Section 3, it is shown that the minimax estimator of p for fixed sample size n is still minimax for relative squared error loss.
openaire   +1 more source

The Role of Ancillarity in Inference for Non-Stationary Variables

The Economic Journal, 1995
Some examples of the regression method are compared with likelihood-based inference. It is shown that, although the asymptotic theory is distinctly different for ergodic and nonergodic processes, the likelihood methods lead to the result that asymptotic inference can be conducted in the same way for the two cases by appealing to classical conditioning ...
openaire   +1 more source

Risk, Sufficiency, Completeness, and Ancillarity

2009
The initial section of this chapter develops a basic framework for inference. Later sections concern the notion of sufficiency that arises when data can be summarized without any loss of information.
openaire   +1 more source

Information, Ancillarity and Conditional Inference

1985
The present chapter studies the amount of information carried by a statistic t(x) from the geometrical point of view. The amount of information plays a fundamental role in parameter estimation and statistical hypothesis testing. Higher-order asymptotic sufficiency, higher-order asymptotic ancillarity, and conditional information are defined in the ...
openaire   +1 more source

The Space of Inference Functions: Ancillarity, Sufficiency and Projection

1988
In this chapter, we construct the space of inference functions and information theoretic notions of E-sufficiency and E-ancillarity within this space. Let X be a sample space, and P be a class of probability measures P on X. For each PeP we let VP be the vector space of real valued functions f defined on the sample space X such that Ep[f(X)]2 < ∞.
D. L. McLeish, Christopher G. Small
openaire   +1 more source