共查询到14条相似文献，搜索用时 0 毫秒
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D. G. Bonett 《Biometrical journal. Biometrische Zeitschrift》1988,30(6):723-727
The Lincoln-Petersen and Bailey estimators of an unknown population size were compared in a computer simulation of capture-recapture sampling with replacement from small populations. The Bailey estimator was negatively biased and had smaller variance than the Petersen estimator. The Petersen estimator tended to be positively biased. The Lincoln-Petersen variance estimator tended to be positively biased while the Bailey variance estimator was negatively biased. 相似文献
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Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study. 相似文献
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G. S. Lingappaiah 《Biometrical journal. Biometrische Zeitschrift》1979,21(4):361-366
k independent samples 0, 1, 2,…,k – 1 (or stages 0, 1,…. k – 1) are available from the exponential population. Each of k – 1 samples, except the sample 0 are censored both above and below. Restricted range is defined as the difference between largest and the smallest among the available observations. Prediction of this restricted range at stage k (in the future sample k) on the basis of the restricted ranges at earlier 1, 2,…k – 1 stages is attempted by obtaining the predictive distribution of this range at stage k. Probability that the variance at stage k is smaller than that at stage k – 1, is evaluated along with the probability integral. An illustrative example is given with simulated samples. 相似文献
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Janine A. Wright Richard J. Barker Matthew R. Schofield Alain C. Frantz rea E. Byrom Dianne M. Gleeson 《Biometrics》2009,65(3):833-840
Summary . Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger ( Meles meles ). 相似文献
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On sampling and the estimation of rare errors 总被引：1，自引：0，他引：1
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Royle JA 《Biometrics》2009,65(1):267-274
Summary . I consider the analysis of capture–recapture models with individual covariates that influence detection probability. Bayesian analysis of the joint likelihood is carried out using a flexible data augmentation scheme that facilitates analysis by Markov chain Monte Carlo methods, and a simple and straightforward implementation in freely available software. This approach is applied to a study of meadow voles ( Microtus pennsylvanicus ) in which auxiliary data on a continuous covariate (body mass) are recorded, and it is thought that detection probability is related to body mass. In a second example, the model is applied to an aerial waterfowl survey in which a double-observer protocol is used. The fundamental unit of observation is the cluster of individual birds, and the size of the cluster (a discrete covariate) is used as a covariate on detection probability. 相似文献