# Order

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Related to Order statistic: Exponential distribution

## Order

Instruction to a broker/dealer to buy, sell, deliver, or receive securities or commodities that commits the issuer of the "order" to the terms specified. See: indication, inquiry, bid wanted, offer wanted.

## Order

An instruction, especially to a broker, to buy, sell, or conduct some other transaction involving a security or commodity. For example, if a client wishes to buy 100 shares in AT&T, he/she makes an order to a broker to that effect. Orders may take a number of forms. A market order is an order to conduct a transaction at the current market price. A limit order is an order to conduct a transaction at a stated price or better. A stop order is an order to conduct a transaction at the best available price after a certain stated price is reached. Finally, a stop-limit order is a stop order that becomes a limit order when the stated price (known as the stop price) is reached.

## order

A customer's instructions to buy or sell securities.
References in periodicals archive ?
The relations for order statistics and lower records are deduced from the relations derived.
The following results, due to Barlow and Proschan (1966), link properties of order statistics to properties of the distribution F.
2] Test for Multiplicative Heteroskedasticity Model Age Group Variance Estimate All 18-34 35-49 50-64 1972 Order statistic 501.
Hawkins' test statistic for detecting multivariate non-normality is based on the n order statistics [A.
in which q = the number of preceding order statistics tied with [X.
1990) "L-moments: Analysis and estimation of distributions using linear combinations or order statistics, " Journal of Royal Statistical Society, Series B Methodology, 52(01): 105-124.
Regional flood frequency analysis using extreme order statistics of the annual peak record.
Order Statistics This table reports information about order submission, cancellation, and execution activity on NASDAQ for sample firms surrounding quote-stuffing events.
Order statistics play a key role in modelling using quantile functions.
Bilkova1: Robust parameter estimations using L- moments, TL-moments and the order statistics, American Journal of Applied Mathematics 2(2), 36-53, 2014.
w] is based on second order statistics, that is class variance and [[sigma].

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