Applied Stochastic Models and Data Analysis

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040201

Shye, Samuel

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040202

Jaraiedi, Majid; Herrin, Gary D.

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040203

The use of signal detection theory (SDT) in evaluation of the performance of an inspector on certain tasks requires the identification of the receiver operating characteristic (ROC) curve of the inspector. In this paper a functional form, Y = f(X), for the ROC equation is proposed, and two alternative approaches are developed for estimation of its parameters. This method does not require prior assumptions on the statistical distribution of the noise and signal‐plus‐noise. The application of the method is illustrated by the use of an example data set.

Jean, William H.; Helms, Billy P.

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040204

In this paper a general method for developing necessary conditions for all degrees of stochastic dominance is derived. The method, a minimization of the expected value of certain functions of the random variable, is used to rederive known necessary conditions for dominance and is then used to derive new necessary conditions. Some of the old and new conditions are then compared empirically using a data set of security returns.

Fox, Dale R.

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040205

Steady‐state probabilities of Markov Processes are computed by enumerating subgraphs of the transition diagram of the process. The presence of cutpoints in the transition diagram allows for decomposition of the problem into smaller components. Examples from queueing theory are presented. Matrix representations for these structures are also discussed.

Mendelson, Haim

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040206

This paper derives a two‐sample non‐parametric procedure for testing quantile‐preserving spreads. The asymptotic properties of the test statistic are studied, and the use of the test is demonstrated in comparing the dispersion of the returns on a stock‐index between months.

Schoentgen, Jean

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040207

The data we deal with are acoustic feature values computed from speech signals produced by normal control speakers and speakers suffering from a laryngeal pathology. The problem is (i) to compare the discriminatory performance of a given acoustic feature with other features of the same family; (ii) to compare the performance of corresponding features in studies carried out on corpora which were not identical and which did not contain the same number of speakers; (iii) to express quantitatively the share of laryngeal pathologies which could be detected reliably by acoustic means alone. We propose to accomplish tasks (i) to (iii) by separating pooled control and risk groups on the basis of the acoustic feature values alone and by evaluating the quality of approximation of the original groups so realized. In order to evaluate this quality we compute a merit factor satisfying the following properties: (1) to be independent of the number of speakers contained in the pooled control and risk groups; (2) to increase and decrease linearly with the detection and false alarm rates, respectively; (3) to take on values between 0 (no discrimination at all) and 1 (perfect discrimination). We also address the question of the confidence one may have in the merit factor values so achieved, i.e. we describe a method of computing the probability that a merit factor value might have been realized by chance alone.

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040208

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040209

1988 Applied Stochastic Models and Data Analysis

doi: 10.1002/asm.3150040210