Some general comparative points on Chao’s and Zelterman’s estimators of population size

Two simple and frequently used capture–recapture estimates of the population size are compared: Chao's lower-bound estimate and Zelterman's estimate allowing for contaminated distributions. In the Poisson case it is shown that if there are only counts of ones and twos, the estimator of Zelterman is always bounded above by Chao's estimator. If counts larger than two exist, the estimator of Zelterman is becoming larger than that of Chao's, if only the ratio of the frequencies of counts of twos and ones is small enough. A similar analysis is provided for the binomial case. For a two-component mixture of Poisson distributions the asymptotic bias of both estimators is derived and it is shown that the Zelterman estimator can experience large overestimation bias. A modified Zelterman estimator is suggested and also the bias-corrected version of Chao's estimator is considered. All four estimators are compared in a simulation study.

Some general comparative points on Chao's and Zelterman's estimators of the population size

Two simple and frequently used capture–recapture estimates of the population size are compared: Chao's lower-bound estimate and Zelterman's estimate allowing for contaminated distributions. In the Poisson case it is shown that if there are only counts of ones and twos, the estimator of Zelterman is always bounded above by Chao's estimator. If counts larger than two exist, the estimator of Zelterman is becoming larger than that of Chao's, if only the ratio of the frequencies of counts of twos and ones is small enough. A similar analysis is provided for the binomial case. For a two-component mixture of Poisson distributions the asymptotic bias of both estimators is derived and it is shown that the Zelterman estimator can experience large overestimation bias. A modified Zelterman estimator is suggested and also the bias-corrected version of Chao's estimator is considered. All four estimators are compared in a simulation study.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Gender stereotyping and decision processes: extending and reversing the gender bias in fame judgements

M. R. Banaji and A. G. Greenwald (1995) demonstrated a gender bias in fame judgments—that is, an increase in judged fame due to prior processing that was larger for male than for female names. They suggested that participants shift criteria between judging men and women, using the more liberal criterion for judging men. This "criterion-shift" account appeared problematic for a number of reasons. In this article, 3 experiments are reported that were designed to evaluate the criterion-shift account of the gender bias in the false-fame effect against a distribution-shift account. The results were consistent with the criterion-shift account, and they helped to define more precisely the situations in which people may be ready to shift their response criterion on an item-by-item basis. In addition, the results were incompatible with an interpretation of the criterion shift as an artifact of the experimental situation in the experiments reported by M. R. Banaji and A. G. Greenwald. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

Language experience shapes the development of the mutual exclusivity bias

Halberda (2003) demonstrated that 17-month-old infants, but not 14- or 16-month-olds, use a strategy known as mutual exclusivity (ME) to identify the meanings of new words. When 17-month-olds were presented with a novel word in an intermodal preferential looking task, they preferentially fixated a novel object over an object for which they already had a name. We explored whether the development of this word-learning strategy is driven by children's experience of hearing only one name for each referent in their environment by comparing the behavior of infants from monolingual and bilingual homes. Monolingual infants aged 17–22 months showed clear evidence of using an ME strategy, in that they preferentially fixated the novel object when they were asked to "look at the dax." Bilingual infants of the same age and vocabulary size failed to show a similar pattern of behavior. We suggest that children who are raised with more than one language fail to develop an ME strategy in parallel with monolingual infants because development of the bias is a consequence of the monolingual child's everyday experiences with words.

Can we harness computerised cognitive bias modification to treat anxiety in schizophrenia? A first step highlighting the role of mental imagery

A new wave of computerised therapy is under development which, rather than simulating talking therapies, uses bias modification techniques to target the core psychological process underlying anxiety. Such interventions are aimed at anxiety disorders, and are yet to be adapted for co-morbid anxiety in psychosis. The cognitive bias modification (CBM) paradigm delivers repeated exposure to stimuli in order to train individuals to resolve ambiguous information in a positive, rather than anxiety provoking, manner. The current study is the first to report data from a modified form of CBM which targets co-morbid anxiety within individuals diagnosed with schizophrenia. Our version of CBM involved exposure to one hundred vignettes presented over headphones. Participants were instructed to actively simulate the described scenarios via visual imagery. Twenty-one participants completed both a single session of CBM and a single control condition session in counter-balanced order. Within the whole sample, there was no significant improvement on interpretation bias of CBM or state anxiety, relative to the control condition. However, in line with previous research, those participants who engage in higher levels of visual imagery exhibited larger changes in interpretation bias. We discuss the implications for harnessing computerised CBM therapy developments for co-morbid anxiety in schizophrenia.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

A revised Tobit procedure for mitigating bias in the presence of non-zero censoring with an application to milk-market participation in the Ethiopian highlands

Fixed transactions costs that prohibit exchange engender bias in supply analysis due to censoring of the sample observations. The associated bias in conventional regression procedures applied to censored data and the construction of robust methods for mitigating bias have been preoccupations of applied economists since Tobin [Econometrica 26 (1958) 24]. This literature assumes that the true point of censoring in the data is zero and, when this is not the case, imparts a bias to parameter estimates of the censored regression model. We conjecture that this bias can be significant; affirm this from experiments; and suggest techniques for mitigating this bias using Bayesian procedures. The bias-mitigating procedures are based on modifications of the key step that facilitates Bayesian estimation of the censored regression model; are easy to implement; work well in both small and large samples; and lead to significantly improved inference in the censored regression model. These findings are important in light of the widespread use of the zero-censored Tobit regression and we investigate their consequences using data on milk-market participation in the Ethiopian highlands. (C) 2004 Elsevier B.V. All rights reserved.