The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Douglas D Heckathorn - One of the best experts on this subject based on the ideXlab platform.
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respondent driven Sampling ii deriving valid population estimates from chain referral samples of hidden populations
Social Problems, 2002Co-Authors: Douglas D HeckathornAbstract:Researchers studying hidden populations–including injection drug users, men who have sex with men, and the homeless–find that standard probability Sampling methods are either inapplicable or prohibitively costly because their subjects lack a Sampling frame, have privacy concerns, and constitute a small part of the general population. Therefore, researchers generally employ non-probability methods, including location Sampling methods such as targeted Sampling, and chain-referral methods such as snowball and respondent-driven Sampling. Though nonprobability methods succeed in accessing the hidden populations, they have been insufficient for statistical inference. This paper extends the respondent-driven Sampling method to show that when biases associated with chain-referral methods are analyzed in sufficient detail, a statistical theory of the Sampling Process can be constructed, based on which the Sampling Process can be redesigned to permit the derivation of indicators that are not biased and have known levels of precision. The results are based on a study of 190 injection drug users in a small Connecticut city.
Arabinda Bhattacharya - One of the best experts on this subject based on the ideXlab platform.
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The effect of uncertainty on the formulation of strategies: a study of selected Indian organizations
SN Business & Economics, 2020Co-Authors: Amit Kundu, Dev Narayan Sarkar, Arabinda BhattacharyaAbstract:An organization’s performance is often claimed to be associated with the business environment, as well as with the organization’s strategic response to such an environment. If the environment of an organization is adverse, it may face challenges in getting its required inputs, and in adding value to the society. The objective of the present study is to propose a measure of uncertainty based on a survey of executives from randomly selected companies and present several implications for practice and theory. The present research follows a multi-stage Sampling Process. In the first stage, a complete list of sectors was taken from CMIE, and four sectors were randomly selected (simple random Sampling) from the list. The selected sectors are as follows: chemical, petro-chemical industry, agricultural-related industry, and food Processing industry. The second stage was also a simple random Sampling Process. The third stage was a sort of convenience Sampling since the top management of the selected organizations proposed the names of the executives to be surveyed. A firm’s performance seems to be associated to the business environment, as well as, the organization’s strategic response to such an environment. There exists a dynamic relationship between a firm and its business environment. Several limitations and future courses of studies are also presented.
Hongwei Sun - One of the best experts on this subject based on the ideXlab platform.
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Regularized Least Square Regression with Unbounded and Dependent Sampling
Abstract and Applied Analysis, 2013Co-Authors: Xiaorong Chu, Hongwei SunAbstract:This paper mainly focuses on the least square regression problem for the -mixing and -mixing Processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from dependent Sampling Process with a more general output data condition. Capacity independent error bounds and learning rates are deduced by means of the integral operator technique.
Amit Kundu - One of the best experts on this subject based on the ideXlab platform.
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The effect of uncertainty on the formulation of strategies: a study of selected Indian organizations
SN Business & Economics, 2020Co-Authors: Amit Kundu, Dev Narayan Sarkar, Arabinda BhattacharyaAbstract:An organization’s performance is often claimed to be associated with the business environment, as well as with the organization’s strategic response to such an environment. If the environment of an organization is adverse, it may face challenges in getting its required inputs, and in adding value to the society. The objective of the present study is to propose a measure of uncertainty based on a survey of executives from randomly selected companies and present several implications for practice and theory. The present research follows a multi-stage Sampling Process. In the first stage, a complete list of sectors was taken from CMIE, and four sectors were randomly selected (simple random Sampling) from the list. The selected sectors are as follows: chemical, petro-chemical industry, agricultural-related industry, and food Processing industry. The second stage was also a simple random Sampling Process. The third stage was a sort of convenience Sampling since the top management of the selected organizations proposed the names of the executives to be surveyed. A firm’s performance seems to be associated to the business environment, as well as, the organization’s strategic response to such an environment. There exists a dynamic relationship between a firm and its business environment. Several limitations and future courses of studies are also presented.
Laura Schulz - One of the best experts on this subject based on the ideXlab platform.
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sensitivity to the Sampling Process emerges from the principle of efficiency
Other repository, 2018Co-Authors: Julian Jaraettinger, Laura Schulz, Felix Sun, Joshua B. TenenbaumAbstract:Humans can seamlessly infer other people's preferences, based on what they do. Broadly, two types of accounts have been proposed to explain different aspects of this ability. The first account focuses on spatial information: Agents' efficient navigation in space reveals what they like. The second account focuses on statistical information: Uncommon choices reveal stronger preferences. Together, these two lines of research suggest that we have two distinct capacities for inferring preferences. Here we propose that this is not the case, and that spatial-based and statistical-based preference inferences can be explained by the assumption that agents are efficient alone. We show that people's sensitivity to spatial and statistical information when they infer preferences is best predicted by a computational model of the principle of efficiency, and that this model outperforms dual-system models, even when the latter are fit to participant judgments. Our results suggest that, as adults, a unified understanding of agency under the principle of efficiency underlies our ability to infer preferences.
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Infants consider both the sample and the Sampling Process in inductive generalization
Proceedings of the National Academy of Sciences of the United States of America, 2010Co-Authors: Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:The ability to make inductive inferences from sparse data is a critical aspect of human learning. However, the properties observed in a sample of evidence depend not only on the true extension of those properties but also on the Process by which evidence is sampled. Because neither the property extension nor the Sampling Process is directly observable, the learner's ability to make accurate generalizations depends on what is known or can be inferred about both variables. In particular, different inferences are licensed if samples are drawn randomly from the whole population (weak Sampling) than if they are drawn only from the property's extension (strong Sampling). Given a few positive examples of a concept, only strong Sampling supports flexible inferences about how far to generalize as a function of the size and composition of the sample. Here we present a Bayesian model of the joint dependence between observed evidence, the Sampling Process, and the property extension and test the model behaviorally with human infants (mean age: 15 months). Across five experiments, we show that in the absence of behavioral cues to the Sampling Process, infants make inferences consistent with the use of strong Sampling; given explicit cues to weak or strong Sampling, they constrain their inferences accordingly. Finally, consistent with quantitative predictions of the model, we provide suggestive evidence that infants' inferences are graded with respect to the strength of the evidence they observe.
