Relevant bibliographies by topics / Heterogeneity

Academic literature on the topic 'Heterogeneity'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Heterogeneity"

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MIMURA, Keiji, Susumu YUKAWA, Akira HIBINO, and Hiroshi NOMOTO. "Heterogeneity of MDA-rich LDL." Journal of Japan Atherosclerosis Society 18, no. 9-10 (1990): 793–802. http://dx.doi.org/10.5551/jat1973.18.9-10_793.

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EDWARDS, J. H. "Heterogeneity." Annals of the New York Academy of Sciences 615, no. 1 Tuberous Scle (April 1991): 344–53. http://dx.doi.org/10.1111/j.1749-6632.1991.tb37777.x.

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Tran, Hien Thu, and Enrico Santarelli. "Spatial heterogeneity, industry heterogeneity, and entrepreneurship." Annals of Regional Science 59, no. 1 (February 28, 2017): 69–100. http://dx.doi.org/10.1007/s00168-017-0819-4.

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Zlatohurska, M. A., and F. I. Tovkach. "Morphological heterogeneity of temperate erwiniophage 59." Mikrobiolohichnyi Zhurnal 78, no. 1 (January 30, 2016): 71–83. http://dx.doi.org/10.15407/microbiolj78.01.071.

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Ni, Xu Dong, Ji Hong Yu, and Xian An Li. "Effects of Speciality Heterogeneity, Education Heterogeneity and Work Experience Heterogeneity on Team Performance." Applied Mechanics and Materials 411-414 (September 2013): 2451–57. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2451.

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This paper started with theories of team heterogeneity and team performance, according to questionnaire survey method, reviewed three dimensions of knowledge heterogeneity (speciality heterogeneity, education heterogeneity and work experience heterogeneity) and their effects on team performance. The result showed that speciality heterogeneity was positively related to task performance and contextual performance, while work experience heterogeneity was positively related to contextual performance significantly, but had no significant relationship with task performance. In addition, education heterogeneity had no significant relationship with task performance and contextual performance.
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Prat, A. "HER2 heterogeneity." Breast 56 (April 2021): S4. http://dx.doi.org/10.1016/s0960-9776(21)00050-3.

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Pe’er, Dana, Seishi Ogawa, Ofer Elhanani, Leeat Keren, Trudy G. Oliver, and David Wedge. "Tumor heterogeneity." Cancer Cell 39, no. 8 (August 2021): 1015–17. http://dx.doi.org/10.1016/j.ccell.2021.07.009.

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Lichtenberk, Frantisek. "Reconstructing Heterogeneity." Oceanic Linguistics 33, no. 1 (June 1994): 1. http://dx.doi.org/10.2307/3622999.

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DELLINGER, JAMES J. "Heterogeneity missing." Journal of Clinical Nursing 11, no. 5 (September 2002): 697. http://dx.doi.org/10.1046/j.1365-2702.2002.00664.x.

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Rowse, Tim. "Indigenous Heterogeneity." Australian Historical Studies 45, no. 3 (September 2, 2014): 297–310. http://dx.doi.org/10.1080/1031461x.2014.946523.

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Dissertations / Theses on the topic "Heterogeneity"

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Lamberti, Giuseppe. "Modelling with heterogeneity." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/309295.

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When collecting survey data for a specific study it is usual to have some background information, in the form for example, of socio-demographic variables. In our context, these variables may be useful in identifying potential sources of heterogeneity. Resolving the heterogeneity may mean to perform distinct analyses based on the main variables for distinct and homogeneous segments of the data, defined in terms of the segmentation variables. In 2009 Gastón Sánchez proposed an algorithm PATHMOX with the aim to automatic detecting heterogeneous segments within the PLS-PM methodology. This technique, based on recursive partitioning, produces a segmentation tree with a distinct path models in each node. At each node PATHMOX searches among all splits based on the segmentation variables and chooses the one resulting in the maximal difference between the PLS-PM models in the children nodes. Starting from the work of Sanchez the purpose of the thesis is to extend PATHMOX in the following points: 1. Extension to the PATHMOX approach to detect which constructs differentiate segments. The PATHMOX approach uses a F-global test to identify the best split in heterogeneous segments. Following the same approach it is possible to extend the testing to find which the endogenous constructs are and which are the relationships between constructs responsible of the difference between the segments. 2. Extension to the PATHMOX approach to deal with the factor invariance problem. Originally PATHMOX adapted the estimation of constructs to each detected segment, that is, once a split is performed the PLS-PM model is recalculated in every child. This leads to the problem of invariance: if the the estimation of the latent variables are recalculated in each terminal node of the tree, we cannot be sure to compare the distinct behavior of two individuals who belong to two different terminal nodes. To solve this problem we will propose a invariance test based on the X^2 distribution, where the goal of to test whether the measurement models of each terminal node can be considered equal or not among them. 3. Extension to the PATHMOX approach to overcome the parametric hypothesis of F-test. One critic to the PATHMOX approach, applied in the context of partial least square path modeling, is that it utilizes a parametric test based on the hypothesis that the residuals have a normal distribution to compare two structural models. PLS-PM in general, is utilized to model data that come from survey analysis. These data are characterized by an asymmetric distribution. This situation produces skewness in the distribution of data. As we know, PLS-PM methodology, is based in the absence of assumptions about the distribution of data. Hence, the parametric F test used in PATHMOX may represent a limit of the methodology. To overcome this limit, we will extend the test in the context of LAD robust regression. 4. Generalization of PATHMOX algorithm to any type of modeling methodology. The PATHMOX algorithm has been proposed to analyze heterogeneity in the context of the partial least square path modeling. However, this algorithm can be applied to many other kind of methodologies according to the appropriate split criterion. To generalize PATHMOX we will consider three distinct scenarios: Regression analysis (OLS, LAD, GLM regression) and Principal Component Analysis. 5. Implement the methodology, using the R software as specific library.
Cuando se realiza un estudio científico, el análisis hace énfasis sobre las variables recogidas para responder a las preguntas que se quieren hallar durante el mismo estudio. Sin embargo en muchos análisis se suele recoger más variables, como por ejemplo variables socio demográfico: sexo, status social, edad. Estas variables son conocidas como variables de segmentación, ya que pueden ser útiles en la identificación de posibles fuentes de heterogeneidad. Analizar la heterogeneidad quiere decir realizar distintas análisis para distintos colectivos homogéneos definidos a partir de las variables de segmentación. Muchas veces, si hay algún conocimiento previo, esta heterogeneidad puede ser controlada mediante la definición de segmentos a priori. Sin embargo no siempre se dispone de conocimiento suficiente para definir a priori los grupos. Por otro lado muchas variables de segmentación podrían ser disponibles para analizar la heterogeneidad de acuerdo con un apropiado algoritmo. Un algoritmo desarrollado con este objetivo fue PATHMOX, propuesto por Gastón Sanchez en 2009. Esta técnica, utilizando particiones recursivas, produce un árbol de segmentación con distintos modelos asociados a cada nodo. Para cada nodo, PATHMOX busca entre todas las variables de segmentación aquella que produce una diferencia máxima entre los modelos de los nodos hijos. Tomando como punto de partida el trabajo de Gastón Sanchez esta tesis se propone: 1. Extender PATHMOX para identificar los constructos responsables de la diferencias. PATHMOX nos permite detectar distintos modelos en un data-set sin identificar grupos a priori. Sin embargo, PATHMOX es un criterio global. Pera identificar las distintas ecuaciones y coeficientes responsables de las particiones, introduciremos los test F-block y F-coefficient. 2. Extender PATHMOX para solucionar el problema de la invariancia. En el contexto del PLS-PM (Partial Least Squares Path Modeling), PATHMOX funciona fijando las relaciones causales entre las variables latentes y el objetivo es identificar modelos con coeficientes path lo más posible distintos sin poner ninguna restricción sobre el modelo de medida. Por lo tanto, cada vez que una diferencia significativa es identificada, y dos nodos hijos vienen definidos, las relaciones causales entre las variables latentes son las mismas en ambos modelos "hijos", pero la estimación de cada variable latente se recalcula y no podemos estar seguros de comparar el comportamiento de dos individuos distintos que pertenecen a dos nodos diferentes. Para resolver este problema propondremos un test de invariancia basado en la distribución X^2, donde el objetivo del test es verificar si los modelos de cada nodo terminales se puede considerar igual o no entre ellos. 3. Extender PATHMOX para superar la hipótesis paramétrica del F-test. Una crítica a PATHMOX, aplicadas en el contexto del PLS-PM, es que el algoritmo utiliza una prueba paramétrica, basada en la hipótesis de que los residuos tienen una distribución normal, para comparar dos modelos estructurales. Para superar este límite, extenderemos el test para comparar dos regresiones robustas LAD en el contexto del PLS. 4. La generalización del algoritmo PATHMOX a cualquier tipo de metodología. El algoritmo PATHMOX ha sido propuesto para analizar la heterogeneidad en el contexto PLS-PM. Sin embargo, este algoritmo se puede aplicar a muchos otros tipos de metodologías de acuerdo con un apropiado criterio de partición. Para generalizar PATHMOX consideraremos tres escenarios distintos: modelos de regresión (modelos OLS, LAD, GLM) y el análisis en componentes principales. 5. Implementar la metodología, utilizando el software R como librería específica.
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Cleutjens, Jacobus Peter Marie. "Basement membrane heterogeneity." Maastricht : Maastricht : Rijksuniversiteit Limburg ; University Library, Maastricht University [Host], 1989. http://arno.unimaas.nl/show.cgi?fid=5472.

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Heslop, Louise. "Satellite cell heterogeneity." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395478.

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Terzi, Ayse, Kees Koedijk, Charles N. Noussair, and Rachel Pownall. "Reference Point Heterogeneity." FRONTIERS MEDIA SA, 2016. http://hdl.handle.net/10150/621274.

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It is well-established that, when confronted with a decision to be taken under risk, individuals use reference payoff levels as important inputs. The purpose of this paper is to study which reference points characterize decisions in a setting in which there are several plausible reference levels of payoff. We report an experiment, in which we investigate which of four potential reference points: (1) a population average payoff level, (2) the announced expected payoff of peers in a similar decision situation, (3) a historical average level of earnings that others have received in the same task, and (4) an announced anticipated individual payoff level, best describes decisions in a decontextualized risky decision making task. We find heterogeneity among individuals in the reference points they employ. The population average payoff level is the modal reference point, followed by experimenter's stated expectation of a participant's individual earnings, followed in turn by the average earnings of other participants in previous sessions of the same experiment. A sizeable share of individuals show multiple reference points simultaneously. The reference point that best fits the choices of the individual is not affected by a shock to her income.
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Shachat, Jason Matthew 1967. "Heterogeneity and equilibrium." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/289566.

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The research reported in this dissertation explores the observable effects that individual heterogeneity implies in strategic environments. The first chapter provides a focused experimental test of mixed strategy play in strictly competitive games. The experiment directly tests whether serial correlation results from subjects' inability to generate sequences of actions that appear to be time independent, or instead from the play of non-equilibrium strategies. This is achieved by allowing the subjects to generate actions via a simple randomizing device. It is found that serial correlation is not reduced and that subjects adopt a wide variety of non-equilibrium mixed strategies. This wide variety of mixtures potentially explains the seeming paradox of minimax winning proportions with a high variance of win rates across pairs of players. In the second chapter a theoretical model is developed for simultaneous move games in which the observable outcomes are allocations of monetary payoffs or commodity bundles, not expected utility levels. It is assumed that the players' mappings from the uncertain money amounts or commodity bundle allocations to expected utility levels are heterogeneous and are private information. The third chapter applies this framework to investigate the incentives to form agricultural marketing pools.
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Boedeker, Peter. "Comparison of Heterogeneity and Heterogeneity Interval Estimators in Random-Effects Meta-Analysis." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157553/.

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Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Accurate estimation of heterogeneity is necessary as a description of the distribution and for determining weights applied in the estimation of the summary effect when using inverse-variance weighting. To evaluate a wide range of estimators, we compared 16 estimators (Bayesian and non-Bayesian) of heterogeneity with regard to bias and mean square error over conditions based on reviews of educational and psychological meta-analyses. Three simulation conditions were varied: (a) sample size per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Confidence or highest density intervals can be calculated for heterogeneity. The heterogeneity estimators that performed best over the widest range of conditions were paired with heterogeneity interval estimators. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. The combination of the Paule Manel estimator and Q-Profile interval method is recommended when synthesizing standardized mean difference effect sizes.
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Yegorov, Yuri. "Spatial Heterogeneity and Equilibrium." Doctoral thesis, Universitat Pompeu Fabra, 1999. http://hdl.handle.net/10803/7592.

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This thesis consists of five chapters, based on four different articles. All of them are devoted to different aspects of spatial heterogeneity and its impact on economic equilibrium in space. The concept of heterogeneous continuous space is discussed in the introductory chapter.
The first model "Equilibrium in Continuous Space under Decentralized Production" addresses the issue of the impact of differences across locations in exogeneous productivity on the structure of equilibrium prices, production and trade. The goal is to describe the general equilibrium in a spatially decentralized economy, when production, consumption and markets are distributed in continuous space and transportation costs are essentially linear. It is shown that an autarky equilibrium can exist only if transport costs are high enough. In the general case, the general equilibrium in this model includes some endogeneously determined trade areas, with flows of goods across space, and autarky areas where production and consumption activities take place only at the same point. An analytical solution in explicit functions is obtained; it contains equilibrium prices, labor supply and flows of goods as functions of the spatial variable. The model can be applied to a set of practical questions in regional economics. In particular, it is able to describe persistent price differentials across regions and non-local consequences of road construction and transportation cost shocks for the economy. The differences across locations in population density may have either historical or economic reasons.
The second model "Hotelling's Revival" extends a well-known research of H.Hotelling (1929) to the two-dimensional case with spatially heterogeneous demand density, preserving the rest of his classical assumptions. It is shown that the problem of demand discontinuity in the one-dimensional model, which was discovered by d'Aspremont, Gabszewich and Thisse (1979), disappears in this case. This also holds for any bounded distribution of consumers on any compact set on a plane, which can describe real geographical situations. Demand continuity still holds for any transport costs, strictly increasing in distance and not necessarily linear. Although this is sufficient for the existence of Nash equilibrium in mixed strategies, in pure strategies it exists only for some subset of cases. Examples of both existence and non-existence are constructed, and for some family of densities the separation point between the two cases is found.
The third model addresses locational choice of heterogeneous consumers, when land is also heterogeneous in quality. It is based on two articles. The first, "Dacha Pricing", is presented in chapter 4 and studies the problem of locational rent in a city-neighbourhood when utility includes both the impact of transport costs and time for transportation. For the case of identical agents the problem is solved explicitly and comparative statics with respect to exogeneous changes in transport cost and speed is studied. For the case of agents who are heterogeneous with respect to their income, a solution is also obtained. The model explains some evidence about dacha pricing in Russia and its dynamics during the transition period. The second article related to this model is "Location and Land Size Choice by Heterogeneous Agents". It generalizes the first one and form a separate chapter 5. A new approach about the general equilibrium allocation of heterogeneous divisible good (like land) among a continuum of heterogeneous consumers is proposed. The model is based on continuity of primitives which allow not only to finding a general equilibrium solution in a class of continuous functions, but also to treat the solution to a continuous problem as the limit of the corresponding sequence of discrete problems. This solves one of Berliant's paradoxes, related to spatial economics. The multiplicity of equilibria is shown to take place.
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Pinto, Eugenio. "Heterogeneity and input reallocation." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3903.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Economics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Jonnalagadda, Deepa. "HETEROGENEITY IN PLATELET EXOCYTOSIS." UKnowledge, 2013. http://uknowledge.uky.edu/biochem_etds/8.

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Platelet exocytosis is essential for hemostasis and for many of its sequelae. Platelets release numerous bioactive molecules stored in their granules enabling them to exert a wide range of effects on the vascular microenvironment. Are these granule cargo released thematically in a context-specific pattern or via a stochastic, kinetically-controlled process? My work describes platelet exocytosis using a systematic examination of platelet secretion kinetics. Platelets were stimulated for increasing times with different agonists (i.e. thrombin, PAR1-agonist, PAR4-agonist, and convulxin) and micro-ELISA arrays were used to quantify the release of 28 distinct α-granule cargo molecules. Agonist potency directly correlated with the speed and extent of release. PAR4-agonist induced slower release of fewer molecules while thrombin rapidly induced the greatest release. Cargo with opposing actions (e.g. pro- and anti-angiogenic) had similar release profiles, suggesting limited thematic response to specific agonists. From the release time-course data, rate constants were calculated and used to probe for underlying patterns. Probability density function and operator variance analyses were consistent with three classes of release events, differing in their rates. The distribution of cargo into these three classes was heterogeneous suggesting that platelet secretion is a stochastic process potentially controlled by several factors such as cargo solubility, granule shape, and/or granule-plasma membrane fusion routes. Sphingosine 1 phosphate (S1P) is a bioactive lipid that is stored in platelets. S1P is essential for embryonic development, vascular integrity, and inflammation. Platelets are an abundant source of S1P due to the absence of the enzymes that degrade it. Platelets release S1P upon stimulation. My work attempts to determine how this bioactive lipid is released from platelets. Washed platelets were stimulated with agonists for defined periods of time and the supernatant and pellet fractions were separated by centrifugation. Lipids were separated by liquid phase extraction and S1P was quantified with a triple quadrapole mass spectrometer. A carrier molecule (BSA) is required to detect release of S1P. Further, there is a dose-dependent increase in total S1P with increasing BSA. S1P release shows characteristics similar to other platelet granule cargo e.g. platelet factor IV (PF4). Platelets from Unc13-d Jinx mice and VAMP8-/- mice, which are secretion-deficient (dense granule, alpha granule and lysosome), were utilized to understand the process of S1P release. S1P release was more affected in Unc13-d Jinx mice mirroring their dense granule secretion defect. Fluorescence microscopy and sub-cellular fractionation were used to examine localization of S1P in platelets. S1P was observed to be enriched in a granule population. These studies indicate the existence of two pools of S1P, a readily extractable agranular pool, sensitive to BSA, and a granular pool that requires the secretion machinery for release. The secretion machinery of platelets in addition to being involved in the release of normal granule cargo is thus proved to be involved in the release of bioactive lipid molecules like S1P.
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Marks, John. "Michel Foucault : towards heterogeneity." Thesis, Nottingham Trent University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357156.

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Books on the topic "Heterogeneity"

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Kolasa, Jurek, and Steward T. A. Pickett, eds. Ecological Heterogeneity. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3062-5.

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Martin, John, and Anthony Trowbridge, eds. Platelet Heterogeneity. London: Springer London, 1990. http://dx.doi.org/10.1007/978-1-4471-1763-6.

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Barteneva, Natasha S., and Ivan A. Vorobjev, eds. Cellular Heterogeneity. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5.

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Jurek, Kolasa, Pickett Steward T. 1950-, and Allen T. F. H, eds. Ecological heterogeneity. New York: Springer-Verlag, 1991.

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Šmajs-Muni, Iztok. Raznorodnost =: Heterogeneity. [Velenje]: Kulturni center I. Napotnik Velenje--Galerija, 2003.

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Turksen, Kursad, ed. Stem Cell Heterogeneity. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6550-2.

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Tavakoli, Vahid. Carbonate Reservoir Heterogeneity. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34773-4.

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Brasier, Allan R., ed. Heterogeneity in Asthma. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4614-8603-9.

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Gordon, Derek, Stephen J. Finch, and Wonkuk Kim. Heterogeneity in Statistical Genetics. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61121-7.

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Boller, Francois, F. Forette, Z. S. Khachaturian, Michel Poncet, and Yves Christen, eds. Heterogeneity of Alzheimer’s Disease. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46776-9.

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Book chapters on the topic "Heterogeneity"

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Shekhar, Shashi, and Hui Xiong. "Heterogeneity." In Encyclopedia of GIS, 425. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_561.

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Orbell, Sheina, Havah Schneider, Sabrina Esbitt, Jeffrey S. Gonzalez, Jeffrey S. Gonzalez, Erica Shreck, Abigail Batchelder, et al. "Heterogeneity." In Encyclopedia of Behavioral Medicine, 961. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100795.

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Jak, Suzanne. "Heterogeneity." In SpringerBriefs in Research Synthesis and Meta-Analysis, 25–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27174-3_3.

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Schneider, Birgit, Beate Löffler, Tino Mager, and Carola Hein. "#HETEROGENEITY." In Digital Humanities Research, 259–60. Bielefeld, Germany: Bielefeld University Press / transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839469132-025.

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Barteneva, Natasha S., and Ivan A. Vorobjev. "Heterogeneity of Metazoan Cells and Beyond: To Integrative Analysis of Cellular Populations at Single-Cell Level." In Cellular Heterogeneity, 3–23. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5_1.

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Dukhinova, Marina, Ekaterina Kopeikina, and Eugene D. Ponomarev. "Usage of Multiparameter Flow Cytometry to Study Microglia and Macrophage Heterogeneity in the Central Nervous System During Neuroinflammation and Neurodegeneration." In Cellular Heterogeneity, 167–77. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5_10.

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Serikbaeva, Anara, Anna Tvorogova, Sholpan Kauanova, and Ivan A. Vorobjev. "Analysis of Microtubule Dynamics Heterogeneity in Cell Culture." In Cellular Heterogeneity, 181–204. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5_11.

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Gladkikh, Aleena, Anastasia Kovaleva, Anna Tvorogova, and Ivan A. Vorobjev. "Heterogeneity of Focal Adhesions and Focal Contacts in Motile Fibroblasts." In Cellular Heterogeneity, 205–18. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5_12.

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Navas-Moreno, Maria, and James W. Chan. "Laser Tweezers Raman Microspectroscopy of Single Cells and Biological Particles." In Cellular Heterogeneity, 219–57. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5_13.

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Bhat, Shabir Ahmad, Iram Khan Iqbal, and Ashwani Kumar. "Quantification of the Metabolic Heterogeneity in Mycobacterial Cells Through the Measurement of the NADH/NAD+ Ratio Using a Genetically Encoded Sensor." In Cellular Heterogeneity, 261–75. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7680-5_14.

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Conference papers on the topic "Heterogeneity"

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Beaumont, Olivier, and Arnold L. Rosenberg. "Link-heterogeneity vs. node-heterogeneity in clusters." In 2010 International Conference on High Performance Computing (HiPC). IEEE, 2010. http://dx.doi.org/10.1109/hipc.2010.5713172.

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Imhof, M. G. "Estimation of 3D Reservoir Heterogeneity Using Seismic Heterogeneity Cubes." In 64th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2002. http://dx.doi.org/10.3997/2214-4609-pdb.5.p221.

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Cook, Eric, Stephanie D. Teasley, and Judith S. Olson. "Heterogeneity in harmony." In the 2005 international ACM SIGGROUP conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1099203.1099265.

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AbuAsal, Sameer, R. Tohid, and J. Ramanujam. "Lost in heterogeneity." In SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2834899.2834904.

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Knipling, Ron, and Anders E. af Wählberg. "The Heterogeneity Principle." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2019. http://dx.doi.org/10.17077/drivingassessment.1699.

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Srinivasa, Ganapati. "Heterogeneity and interconnect." In the International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2347655.2347656.

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Sott, Kristin, Emma Eriksson, and Mattias Goksör. "Visualizing Cellular Heterogeneity." In Asia Optical Fiber Communication and Optoelectronic Exposition and Conference. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/aoe.2008.sui2.

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Boonpinon, Nuttapon, and Attawith Sudsang. "Heterogeneity Driven Circular Formation." In 2006 IEEE International Conference on Robotics and Biomimetics. IEEE, 2006. http://dx.doi.org/10.1109/robio.2006.340360.

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Xu, Xinpeng. "INSTITUTIONS AND EXPORT HETEROGENEITY." In IISES Annual Conference, Sevilla, Spain. International Institute of Social and Economic Sciences, 2018. http://dx.doi.org/10.20472/iac.2018.035.049.

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Kummerfeld, Sarah K., Jason A. Hackney, Michael J. Townsend, and Hilary F. Clark. "Rheumatoid Arthritis molecular heterogeneity." In the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854907.

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Reports on the topic "Heterogeneity"

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Manresa, Elena, Thibaut Lamadon, and Stéphane Bonhomme. Discretizing unobserved heterogeneity. The IFS, March 2017. http://dx.doi.org/10.1920/wp.ifs.2017.1703.

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Wen, Yi. When does Heterogeneity Matter? Federal Reserve Bank of St. Louis, 2009. http://dx.doi.org/10.20955/wp.2009.024.

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Krueger, Dirk, Kurt Mitman, and Fabrizio Perri. Macroeconomics and Household Heterogeneity. Cambridge, MA: National Bureau of Economic Research, June 2016. http://dx.doi.org/10.3386/w22319.

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Ahn, Hie Joo, and James Hamilton. Heterogeneity and Unemployment Dynamics. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22451.

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Bils, Mark, Yongsung Chang, and Sun-Bin Kim. Heterogeneity and Cyclical Unemployment. Cambridge, MA: National Bureau of Economic Research, July 2009. http://dx.doi.org/10.3386/w15166.

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Benabou, Roland. Heterogeneity, Stratification, and Growth. Cambridge, MA: National Bureau of Economic Research, April 1993. http://dx.doi.org/10.3386/w4311.

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Chang, Minsu, Xiaohong Chen, and Frank Schorfheide. Heterogeneity and Aggregate Fluctuations. Cambridge, MA: National Bureau of Economic Research, May 2021. http://dx.doi.org/10.3386/w28853.

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Kaymak, Barış, and Immo Schott. Tax Heterogeneity and Misallocation. Federal Reserve Bank of Cleveland, December 2023. http://dx.doi.org/10.26509/frbc-wp-202333.

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Abstract:
Companies face different effective marginal tax rates on their income. This can be detrimental to allocative efficiency unless taxes offset other distortions in the economy. This paper estimates the effect of tax rate heterogeneity on aggregate productivity in distorted economies with multiple frictions. Using firm-level balance-sheet data and estimates of marginal tax rates, we find that tax heterogeneity reduces total factor productivity by about 3 percent. Our findings highlight the positive correlation between marginal tax rates and other distortions to capital and especially labor. This implies that tax rate heterogeneity exacerbates the distortionary effects of other frictions in the economy.
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Vetter, Jeffrey S., Ron Brightwell, Maya Gokhale, Pat McCormick, Rob Ross, John Shalf, Katie Antypas, et al. Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1473756.

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Beraja, Martin, Andreas Fuster, Erik Hurst, and Joseph Vavra. Regional Heterogeneity and Monetary Policy. Cambridge, MA: National Bureau of Economic Research, March 2017. http://dx.doi.org/10.3386/w23270.

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