๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Asymptotic normality of maximum likelihood estimators from multiparameter response-driven designs

โœ Scribed by William F. Rosenberger; Nancy Flournoy; Stephen D. Durham

Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
541 KB
Volume
60
Category
Article
ISSN
0378-3758

No coin nor oath required. For personal study only.

โœฆ Synopsis

Estimation

and inference for dependent trials are important issues in response-adaptive allocation designs; maximum likelihood estimation is one route of interest. We present three nova1 response-driven designs and derive their maximum likelihood estimators. We also provide convenient regularity conditions that ensure the maximum likelihood estimator from a multiparameter stochastic process exists and is asymptotically multivariate normal. While these conditions may not be the most general, they are easily verified for our applications.

๐Ÿ“œ SIMILAR VOLUMES

Maximum Likelihood Estimation of Means a
Maximum Likelihood Estimation of Means and Variances from Normal Populations Under Simultaneous Order Restrictions
โœ N.Z. Shi ๐Ÿ“‚ Article ๐Ÿ“… 1994 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 380 KB

For \(k\) normal populations with unknown means \(\mu_{i}\) and unknown variances \(\sigma_{t}^{2}\), \(i=1, \ldots, k\), assume that there are some order restrictions among the means and variances, respectively, for example, simple order restrictions: \(\mu_{1} \leqslant \mu_{2} \leqslant \cdots \l