MDA

(redirected from Multiple discriminant analysis)
Also found in: Dictionary, Financial, Encyclopedia, Wikipedia.

methylenedioxyamphetamine

 (MDA) [meth″il-di-ok″se-am-fet´ah-mēn]
a hallucinogenic compound chemically related to amphetamine and mescaline; it is widely abused and causes dependence.

MDA

A phenethylamine and amphetamine class of psychoactive agent, used primarily as a recreational drug but also to facilitate transcendence, meditation, psychedelic psychotherap and psychonautics. There are no medical indications for MDA.

Adverse effects
Cardiovascular and CNS stimulation; acute effects include agitation, sweating, hypertension, tachycardia, hyperthermia, convulsions, death.

MDA

1. Mento-dextra anterior–obstetrics.
2. Methydopamine.
3. 3,4-Methylenedioxymethamphetamine. See Ecstasy.
References in periodicals archive ?
How well do financial ratios and multiple discriminant analysis predict company failures in Malaysia.
After reviewing the 86 studies, the study concluded that multiple discriminant analysis is the number one and binary regression (logit analysis) is the second among sixteen techniques for developing a new bankruptcy prediction model used by the Altman's (1968) and Ohlson's (2010) were two best and efficient.
In the multiple discriminant analysis, which aims to detect the discriminative ability or explanatory power of the variables, we used the SPSS[R] software, which generates a discriminant function and the eigenvalue of the data.
Therefore PLS can be used to operationalize a confirmatory form of multiple discriminant analysis. However, given the current computer programs available for PLS, the operationalization of this approach is cumbersome and will not be discussed here.
Classification results and coefficients for the eight-variable classification function for the logit and multiple discriminant analysis models one year prior to insolvency are presented in Table 1.(6) Results were validated using the Lachenbruch method, the V-fold method, and the estimation/holdout sample method.(7) Examining the results for the full sample indicates that the logit model outperforms multiple discriminant analysis, misclassifying fewer solvent and insolvent insurers.
A summary of the step-wise multiple discriminant analysis is shown in Table II.
Table 2 Multiple Discriminant Analysis Summary Equivalent Step Variable Entered Wilk's Lambda F P 1 Attitude Toward Employment .86 13.00 .001 2 Attitude Towed Seeking and .84 7.42 .001 Receiving Services (*) 3 Self-Esteem and Attitude Toward Self .83 5.28 .001 * No other Variables included after Step 3 due to insufficient tolerance
The traditional approach and present standard for predicting financial distress uses multiple discriminant analysis (MDA) to weight the relative value of information provided by a combination of financial ratios.(1) But MDA has been sharply criticized because the validity of its results hinges on restrictive assumptions (Werbos |37~, Eisenbeis |11~, Altman and Eisenbeis |3~, Scott |29~, Tollefson and Joy |32~, Sheth |31~, Ohlson |26~, Pinches |27~, Zmijewski |41~, Zavgren |39~, Karels and Prakash |17~, and Odom and Sharda |25~).
Chief among these methods have been multiple regression analysis, multiple discriminant analysis and gravity models.
A multiple discriminant analysis is a statistical procedure that is frequently used to distinguish (discriminate) between two or more populations on the basis of observations on several variables.
Altman's concept which analyzes various financial ratios using multiple discriminant analysis to predict corporate bankruptcy and James Kristy's Commercial Credit Matrix which involves the following ratios: current, quick, cash, equity/debt, and return on equity.
For example, Trieschmann and Pinches (1973) report that their multiple discriminant analysis (MDA) model correctly classifies 92 percent of insolvent insurers and 96 percent of solvent firms two years prior to the determination of insolvency or solvency; later studies report correct classifications ranging from 62 to 100 percent.

Medical browser ?
Full browser ?