Efficiency of estimation when there is only one common factor

Efficiency of estimation when there is only one common factor

by Frederic M. Lord, Marilyn S. Wingersky

Part of Research bulletin -- RB-71-16

Join Readfeed freeFind clubs reading this
Rate this book:

Browse books you can read free on Readfeed

No club is reading this yet — be the first to start one

Start a club free
About
Explicit formulas are derived for the asymptotic sampling variances and covariances of the maximum likelihood estimators for factor-analysis parameters in the special case where there is just one common factor. The effect of the number of variables on these variances and covariances is indicated. A formula is given showing to what extent the usual covariance between two of a set of variables can be estimated more efficiently when there is known to be just one common factor.

Discuss Efficiency of estimation when there is only one common factor with other readers

Join or start a book club for Efficiency of estimation when there is only one common factor on Readfeed. Live chat, shared reading progress, and AI discussion questions — free to get started.

Sign up freeBrowse book clubs

Frequently asked questions

How do I join a book club for Efficiency of estimation when there is only one common factor?

Sign up free on Readfeed, then browse public clubs or start your own club with Efficiency of estimation when there is only one common factor as the current read. Invite friends with a share link and discuss together with live chat and AI discussion questions.

Can I discuss Efficiency of estimation when there is only one common factor with other readers online?

Yes. Readfeed book clubs let you chat live, share progress, and join discussions about Efficiency of estimation when there is only one common factor with readers worldwide — whether your club is virtual, in-person, or hybrid.

Is Readfeed free?

Yes. Creating an account and joining book clubs is free. Sign up to find readers who love the same books and start discussing today.