Design and Estimation in Small Sample Quantal Response Problems: A Monte Carlo Study

In sensitivity testing for the Department of Defense, the high cost of experimental units necessitates the use of small sample sizes and accentuates the importance of design. This paper compares five data collection--estimation procedures. Four of these are modifications of the Robbins-Monro method,...

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Hauptverfasser:	Bodt, Barry A, Tingey, Henry B
Format:	Report
Sprache:	eng
Schlagworte:	BIOASSAY BIOLOGICAL ASSAY Biology COMBINATORIAL ANALYSIS DATA ACQUISITION DEPARTMENT OF DEFENSE DESIGN OF EXPERIMENTS DOSE-RESPONSE ERRORS FUNCTIONS GATES(CIRCUITS) HIGH COSTS LETHAL DOSAGE MAXIMUM LIKELIHOOD ESTIMATION MEAN MEDIAN EFFECTIVE DOSE MEDIAN LETHAL DOSE MONTE CARLO METHOD QUANTAL RESPONSE TESTING RESPONSE SENSITIVITY SENSITIVITY TESTING SEQUENTIAL DESIGNS SIMULATION STATISTICAL SAMPLES Statistics and Probability TEST AND EVALUATION TEST METHODS WIDTH
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Zusammenfassung:	In sensitivity testing for the Department of Defense, the high cost of experimental units necessitates the use of small sample sizes and accentuates the importance of design. This paper compares five data collection--estimation procedures. Four of these are modifications of the Robbins-Monro method, and the other is the Langlie. The simulation study is designed as a factorial experiment with response function, sample size, initial design point, gate width, and noise as factors. The estimated V sub 50 and its root mean square error are the responses compared to assess the small sample behavior of each method. Although there is no single clear-cut winner, the Delayed Robbins-Monro with maximum likelihood estimation and the Estimated Quantal Response Curve (Wu 1985) are shown to perform well over a broad variety of conditions.