Estimation of Error Rates in Discriminant Analysis

Lachenbruch, Peter A.; Mickey, M. R.

doi:10.2307/1266219

Cited by 591 publications

(333 citation statements)

References 0 publications

Supporting

Mentioning

325

Contrasting

Unclassified

Order By: Relevance

“…This discriminant function was associated with an overall correct classification rate of 75% of total cases (N = 60). A jackknife classification (Lachenbruch & Mickey, 1968) indicated that 75.0% of cases were classified correctly (mild = 71.470, moderate = 70.0%, severe = 84.2%).…”

Section: Resultsmentioning

confidence: 99%

Five psychosocial variables related to the existence of Post-Traumatic Stress Disorder symptoms

Green

Berlin

1987

J. Clin. Psychol.

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Five psychosocial variables related to the existence of Post-Traumatic Stress Disorder symptoms

Green

Berlin

1987

J. Clin. Psychol.

View full text Add to dashboard Cite

“…Unbiased estimates of these classiÿcation probabilities can be obtained using jack-knife estimates when the estimated function is applied to the training data [16] or using cross-validation when a hold-out sample is available. Such estimates can also be obtained in the longitudinal data setting, but additional possibilities for evaluating classiÿcation arise in this context and these will be the focus of this section.…”

Section: Evaluating the Longitudinal Discriminant Methodsmentioning

confidence: 99%

Linear discriminant models for unbalanced longitudinal data

Marshall

Barón

2000

Statist. Med.

View full text Add to dashboard Cite

“…Due to the small size of the data set the leave-one-out method (Lachenbruch and Mickey 1968) was used. Given the number of data sets n, the bud break dates of one data set were predicted using model parameters (threshold temperature, starting date and accumulated heat sum) derived from all but this (n−1) data set.…”

Section: Cross Validationmentioning

confidence: 99%

Grapevine bud break prediction for cool winter climates

Nendel

2009

Int J Biometeorol

View full text Add to dashboard Cite

Statistical analysis of bud break data for grapevine (Vitis vinifera L. cvs. Riesling and Müller-Thurgau) at 13 sites along the northern boundary of commercial grapevine production in Europe revealed that, for all investigated sites, the heat summation method for bud break prediction can be improved if the starting date for the accumulation of heat units is specifically determined. Using the coefficient of variance as a criterion, a global minimum for each site can be identified, marking the optimum starting date. Furthermore, it was shown that the application of a threshold temperature for the heat summation method does not lead to an improved prediction of bud break. Using site-specific parameters, bud break of grapevine can be predicted with an accuracy of +/- 2.5 days. Using average parameters, the prediction accuracy is reduced to +/- 4.5 days, highlighting the sensitivity of the heat summation method to the quality and the representativeness of the driving temperature data.

show abstract

Estimation of Error Rates in Discriminant Analysis

Cited by 591 publications

References 0 publications

Five psychosocial variables related to the existence of Post-Traumatic Stress Disorder symptoms

Five psychosocial variables related to the existence of Post-Traumatic Stress Disorder symptoms

Linear discriminant models for unbalanced longitudinal data

Grapevine bud break prediction for cool winter climates

Contact Info

Product

Resources

About