The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inap...The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inappropriate objective function in data fitting,lack of unique estimates due to the difficulty in finding global minima in minimization,biased estimates due to outliers,and estimations of selectivity being influenced by the predetermined selectivity functions.In this study,we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity.The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers.According to the estimated gillnet selectivity curve,the selectivity function can also be determined.This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool.We suggest the proposed approach be used in estimating gillnet selectivity.展开更多
Variables among the macroclimate, microclimate and rice canopy categories and three other different farming systems were evaluated on their effects to the egg and larval density of Aedes spp. mosquitoes known as trans...Variables among the macroclimate, microclimate and rice canopy categories and three other different farming systems were evaluated on their effects to the egg and larval density of Aedes spp. mosquitoes known as transmitters of animal and human diseases. No statistical difference in egg density （#eggs/mL） among farming systems （P = 0.345） were observed. However, there was significant difference in larval density （#1arvae/mL） among farming systems （P 〈 0.001） particularly between organic and conventional farms and between organic and mixed farms at （P 〈 0.05）. Among the variables in the macroclimate category, wind velocity and ambient temperature significantly influenced larval density in conventional farms. Among the variables in the microclimate category, water temperature significantly contributed to larval density in both the mixed and conventional farms whereas water turbidity, in conventional farms. Among the variables in the rice canopy category, the number of tillers per plant was a significant contributor to larval density in all farm types. No variable among the environmental exposure categories affected the larval density in organic farms.展开更多
Dalian, Shenyang, Changchun and Harbin are the four core cities which play an essential role in terms of promoting the economic development in Northeast China. In this paper, the impact of urban agglomeration on labor...Dalian, Shenyang, Changchun and Harbin are the four core cities which play an essential role in terms of promoting the economic development in Northeast China. In this paper, the impact of urban agglomeration on labor productivity is explored by making comparisons among these four cities. The model used for analysis is a classical model derived from previous studies. Some indicators, such as population density and economic density, were selected to examine the impact of urban agglomeration on the labor productivity based on the time-series data for the four cities from 1990 to 2007. The four main conclusions are： l） The promotion from the growth rate of population density on the growth rate of labor productivity is limited. 2） The negative relationship exists between the growth rate of employment density and the growth rate of labor productivity. 3） Agglomeration effect exists in the four cities, the highest one is Dalian, Shenyang takes the second place, followed by Changchun and Harbin, and the predominant promotion exerted on the labor productivity is the output density.展开更多
We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypothes...We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypotheses and the acceptance of one and the rejection of the other is a framework which can often be faulty when such inferences are applied to individual subjects. This follows from noting that the statistical inferential framework is predominantly based on conclusions drawn for aggregates and noting that what is true in the aggregate frequently does not hold for individuals, an ecological fallacy. Such a fallacy is usually seen as problematic when each data record represents aggregate statistics for counties or districts and not data for individuals. Here we demonstrate strong ecological fallacies even when using subject data. Inverted simulations, of trials rightly sized to detect meaningful differences, yielding a statistically significant p-value of 0.000001 （1 in a million） and associated with clinically meaningful differences between a hypothetical new therapy and a standard therapy, had a proportion of instances of subjects with standard therapy effect better than new therapy effects close to 30%. A ―winner take all‖ choice between two hypotheses may not be supported by statistically significant differences based on stochastic data. We also argue the incorrectness across many individuals of other summaries such as correlations, density estimates, standard deviations and predictions based on machine learning models. Despite artifacts we support the use of prospective clinical trials and careful unbiased model building as necessary first steps. In health care, high touch personalized care based on patient level data will remain relevant even as we adopt more high tech data-intensive personalized therapeutic strategies based on aggregates.展开更多
基金Supported by National Key Technology R&D Program of China(No.2006BAD09A05)
文摘The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inappropriate objective function in data fitting,lack of unique estimates due to the difficulty in finding global minima in minimization,biased estimates due to outliers,and estimations of selectivity being influenced by the predetermined selectivity functions.In this study,we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity.The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers.According to the estimated gillnet selectivity curve,the selectivity function can also be determined.This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool.We suggest the proposed approach be used in estimating gillnet selectivity.
文摘Variables among the macroclimate, microclimate and rice canopy categories and three other different farming systems were evaluated on their effects to the egg and larval density of Aedes spp. mosquitoes known as transmitters of animal and human diseases. No statistical difference in egg density （#eggs/mL） among farming systems （P = 0.345） were observed. However, there was significant difference in larval density （#1arvae/mL） among farming systems （P 〈 0.001） particularly between organic and conventional farms and between organic and mixed farms at （P 〈 0.05）. Among the variables in the macroclimate category, wind velocity and ambient temperature significantly influenced larval density in conventional farms. Among the variables in the microclimate category, water temperature significantly contributed to larval density in both the mixed and conventional farms whereas water turbidity, in conventional farms. Among the variables in the rice canopy category, the number of tillers per plant was a significant contributor to larval density in all farm types. No variable among the environmental exposure categories affected the larval density in organic farms.
基金Under the auspices of National Natural Science Foundation of China (No. 41071088)National Social Science Foundation of China (No. 08BJY056)
文摘Dalian, Shenyang, Changchun and Harbin are the four core cities which play an essential role in terms of promoting the economic development in Northeast China. In this paper, the impact of urban agglomeration on labor productivity is explored by making comparisons among these four cities. The model used for analysis is a classical model derived from previous studies. Some indicators, such as population density and economic density, were selected to examine the impact of urban agglomeration on the labor productivity based on the time-series data for the four cities from 1990 to 2007. The four main conclusions are： l） The promotion from the growth rate of population density on the growth rate of labor productivity is limited. 2） The negative relationship exists between the growth rate of employment density and the growth rate of labor productivity. 3） Agglomeration effect exists in the four cities, the highest one is Dalian, Shenyang takes the second place, followed by Changchun and Harbin, and the predominant promotion exerted on the labor productivity is the output density.
文摘We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypotheses and the acceptance of one and the rejection of the other is a framework which can often be faulty when such inferences are applied to individual subjects. This follows from noting that the statistical inferential framework is predominantly based on conclusions drawn for aggregates and noting that what is true in the aggregate frequently does not hold for individuals, an ecological fallacy. Such a fallacy is usually seen as problematic when each data record represents aggregate statistics for counties or districts and not data for individuals. Here we demonstrate strong ecological fallacies even when using subject data. Inverted simulations, of trials rightly sized to detect meaningful differences, yielding a statistically significant p-value of 0.000001 （1 in a million） and associated with clinically meaningful differences between a hypothetical new therapy and a standard therapy, had a proportion of instances of subjects with standard therapy effect better than new therapy effects close to 30%. A ―winner take all‖ choice between two hypotheses may not be supported by statistically significant differences based on stochastic data. We also argue the incorrectness across many individuals of other summaries such as correlations, density estimates, standard deviations and predictions based on machine learning models. Despite artifacts we support the use of prospective clinical trials and careful unbiased model building as necessary first steps. In health care, high touch personalized care based on patient level data will remain relevant even as we adopt more high tech data-intensive personalized therapeutic strategies based on aggregates.
