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Books on the topic 'Biased estimates'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Logan, Trevon D. Are Engel curve estimates of CPI bias biased? National Bureau of Economic Research, 2008.

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2

Blackburn, McKinley L. Are OLS estimates of the return to schooling biased downward?: Another look. National Bureau of Economic Research, 1993.

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3

Diskin, Boris. Solving upwind-biased discretizations II: Multigrid solver using semicoarsening. National Aeronautics and Space Administration, Langley Research Center, 1999.

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4

Center, Langley Research, ed. Solving upwind-biased discretizations II: Multigrid solver using semicoarsening. National Aeronautics and Space Administration, Langley Research Center, 1999.

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5

Center, Langley Research, ed. Solving upwind-biased discretizations II: Multigrid solver using semicoarsening. National Aeronautics and Space Administration, Langley Research Center, 1999.

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6

Center, Langley Research, ed. Solving upwind-biased discretizations II: Multigrid solver using semicoarsening. National Aeronautics and Space Administration, Langley Research Center, 1999.

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7

Heckman, James J. Bias corrected estimates of GED returns. National Bureau of Economic Research, 2006.

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8

Neumark, David. Attrition bias in economic relationships estimated with matched CPS files. National Bureau of Economic Research, 2001.

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9

National Center for Health Statistics (U.S.). Nonresponse bias in estimates from the 2012 National Ambulatory Medical Care Survey. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2016.

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10

Grosh, Margaret E. An observation on the bias in clinic-based estimates of malnutrition rates. Technical Dept., Latin America and the Caribbean Regional Office, World Bank, 1991.

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11

Ashenfelter, Orley. A review of estimates of the schooling/earnings relationship, with tests for publication bias. Industrial Relations Section, Princeton University, 1999.

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12

Mishra, Vinod K. Evaluating HIV estimates from national population-based surveys for bias resulting from non-response. Marco International, 2008.

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13

Mishra, Vinod K. Evaluating HIV estimates from national population-based surveys for bias resulting from non-response. Marco International, 2008.

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14

Ashenfelter, Orley. A review of estimates of the schooling/earnings relationship with tests for publication bias. National Bureau of Economic Research, 2000.

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15

Hammell, Larry. The relative bias in sampling estimates of production and disease parameters of caged Atlantic salmon. University of Prince Edward Island, 1992.

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16

Rebucci, Alessandro. On t he heterogeneity bias of pooled estimators in stationary VAR specifications. International Monetary Fund, Research Department, 2003.

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17

Conley, Dalton. The equal environments assumption in the post-genomic age: Using misclassified twins to estimate bias in heritability models. National Bureau of Economic Research, 2011.

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18

Roussos, Louis A. Theoretical formula for statistical bias in CATSIB DIF estimator due to discretization of the ability scale. Law School Admission Council, 2006.

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19

Office, General Accounting. Consumer Price Index: Update of Boskin Commission's estimate of bias : report to the Ranking Minority Member, Committee on Finance, U.S. Senate. The Office, 2000.

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20

Orme, Chris. A note on adjusting the bias of maximum likelihood estimators in discrete panel data models with unobserved random effects. Loughborough University of Technology, Department of Economics, 1992.

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21

Dufour, Jean-Marie. On estimators of the disturbance variance in econometric models: Some general small-sampleresults on bias and the existence of moments. CORE, 1985.

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22

Canada. Dept. of Fisheries and Oceans. Analysis of Gear Selectivity and Sources of Bias in Estimates of Age and Stock Composition of the 1980: 1984 Barkley Sound Sockeye Salmon (Oncorhynchus Nerka) Catch. s.n, 1986.

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23

Brick, John Michael. Undercoverage bias in estimates of characteristics of adults and of adults and 0- to 2-year-olds in the 1995 National Household Education Survey (NHES:95). U.S. Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics, 1996.

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24

Mapeta, W. T. An evaluation of the 2005-06 Zimbabwe Demographic and Health Survey HIV prevalence estimates for potential bias due to non-response and exclusion of non-household population. s.n., 2010.

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25

Solving upwind-biased discretizations II: Multigrid solver using semicoarsening. National Aeronautics and Space Administration, Langley Research Center, 1999.

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26

Cheng, Russell. Finite Mixture Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0017.

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Fitting a finite mixture model when the number of components, k, is unknown can be carried out using the maximum likelihood (ML) method though it is non-standard. Two well-known Bayesian Markov chain Monte Carlo (MCMC) methods are reviewed and compared with ML: the reversible jump method and one using an approximating Dirichlet process. Another Bayesian method, to be called MAPIS, is examined that first obtains point estimates for the component parameters by the maximum a posteriori method for different k and then estimates posterior distributions, including that for k, using importance sampli

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27

Cernat, Alexandru, and Joseph W. Sakshaug, eds. Measurement Error in Longitudinal Data. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198859987.001.0001.

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Understanding change is essential in most scientific fields. This is highlighted by the importance of issues such as shifts in public health and changes in public opinion regarding politicians and policies. Nevertheless, our measurements of the world around us are often imperfect. For example, measurements of attitudes might be biased by social desirability, while estimates of health may be marred by low sensitivity and specificity. In this book we tackle the important issue of how to understand and estimate change in the context of data that are imperfect and exhibit measurement error. The bo

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28

Cheng, Russell. Box-Cox Transformations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0010.

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This chapter examines the well-known Box-Cox method, which transforms a sample of non-normal observations into approximately normal form. Two non-standard aspects are highlighted. First, the likelihood of the transformed sample has an unbounded maximum, so that the maximum likelihood estimate is not consistent. The usually suggested remedy is to assume grouped data so that the sample becomes multinomial. An alternative method is described that uses a modified likelihood similar to the spacings function. This eliminates the infinite likelihood problem. The second problem is that the power trans

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29

Fermüller, Cornelia. Motion Illusions in Man and Machine. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0006.

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At the level of mathematical abstraction, computing image motion amounts to an estimation problem and can be analyzed using the tools of statistics and signal processing. As shown in this chapter, intrinsic limitations to the estimation processes make it impossible to derive veridical estimates for all images. Image motion is estimated erroneously, and as a result higher level processes compute erroneous three-dimensional motion and moving scenes. Specifically, two limitations are discussed: (a) due to noise in image data, there is statistical bias that affects anisotropic patterns and (2) the

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30

Walsh, Bruce, and Michael Lynch. Individual Fitness and the Measurement of Univariate Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0029.

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This chapter examines various estimates of the fitness of an individual, focusing on statistical issues and potential sources of bias. With estimates of individual fitness in hand, one can then search for fitness-trait association, and this topic comprises the second half of the chapter. A number of metrics for describing how the phenotypic distribution of a trait is perturbed by selection are examined, again along with a discussion of statistical issues and sources of bias.

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31

Sogunro, Babatunde Oluwasegun. Nonresponse in industrial censuses in developing countries: Some proposals for the correction of biased estimators. 1988.

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32

Walsh, Bruce, and Michael Lynch. Using Molecular Data to Detect Selection: Signatures from Multiple Historical Events. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0010.

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This chapter examines the search for a pattern of repetitive adaptive substitutions over evolutionary time. In contrast with the previous chapter, only a modest number of tests toward this aim have been proposed. The HKA and McDonald-Kreitman tests contrast the polymorphism to divergence ratio between different genomic classes (such as different genes or silent versus replacement sites within the same gene). These approaches can detect an excess of substitutions, which allows one to estimate the fraction of adaptive sites. This chapter reviews the empirical data on estimates of this fraction a

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33

Milusheva, Sveta, Daniel Björkegren, and Leonardo Viotti. Assessing Bias in Smartphone Mobility Estimates in Low Income Countries. Association for Computing Machinery, 2021. https://doi.org/10.1596/36611.

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34

Brown, Andrew W., Tapan S. Mehta, and David B. Allison. Publication Bias in Science. Edited by Kathleen Hall Jamieson, Dan M. Kahan, and Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.10.

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When we rely on science to inform decisions about matters such as the environment, teaching strategies, economics, government, and medicine, evidence-based decision-making can only be as reliable as the totality of the science itself. We must avoid distortions of the scientific literature such as publication bias, which is an expected systematic difference between estimates of associations, causal effects, or other quantities of interest compared to the actual values of those quantities, caused by differences between research that is published and the totality of research conducted. Publicatio

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35

Consumer price index: Update of Boskin Commission's estimate of bias. U.S. General Accounting Office, 2000.

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36

Kingsbury, Nancy R. Consumer Price Index: Update of Boskin Commissionªs Estimate of Bias. Diane Pub Co, 2000.

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37

Taberlet, Pierre, Aurélie Bonin, Lucie Zinger, and Eric Coissac. The future of eDNA metabarcoding. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198767220.003.0019.

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Environmental DNA-based research is undergoing rapid developments, but its democratization in basic and applied research remains hampered by the biases introduced by molecular approaches, the difficulties in estimating absolute organisms’ abundances, and a lack of general consensus in molecular protocols. Chapter 19 “The future of eDNA metabarcoding” provides an overview of these current challenges and discusses how shotgun sequencing, capture-based methods, inclusion of internal standards, and development of new data repositories could alleviate these limits and facilitate cross-experiments c

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38

Rebucci, Alessandro. On the Heterogeneity Bias of Pooled Estimators in Stationary Var Specifications. International Monetary Fund, 2003.

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39

Rebucci, Alessandro. On the Heterogeneity Bias of Pooled Estimators in Stationary Var Specifications. International Monetary Fund, 2003.

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40

Rebucci, Alessandro. On the Heterogeneity Bias of Pooled Estimators in Stationary Var Specifications. International Monetary Fund, 2003.

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41

Findley-Klein, Cynthia T. Optimistic bias and precautionary behavior: A longitudinal study of comparative vs. non-comparative risk estimates. 1999.

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42

Corral, Paul. When Aggregation Misleads: Bias in Unit-Level Small Area Estimates of Poverty with Aggregate Data. Washington, DC: World Bank, 2025. https://doi.org/10.1596/1813-9450-11110.

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43

Undercoverage bias in estimates of characteristics of households and adults in the 1996 National Household Education Survey. U.S. Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics, 1997.

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44

Gelman, Andrew, and Deborah Nolan. Statistical inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0009.

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This chapter begins with a very successful demonstration that illustrates many of the general principles of statistical inference, including estimation, bias, and the concept of the sampling distribution. Students each take a “random” sample of different size candies, weigh them, and estimate the total weight of all candies. Then various demonstrations and examples are presented that take the students on the transition from probability to hypothesis testing, confidence intervals, and more advanced concepts such as statistical power and multiple comparisons. These activities include use an infl

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45

Morral, Andrew R., Terry L. Schell, and Kristie L. Gore. Sexual Assault and Sexual Harassment in the U.S. Military: Investigations of Potential Bias in Estimates from the 2014 RAND Military Workplace Stud. RAND Corporation, 2016.

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46

The Role of Selection Bias in Estimates of the Deterrence Effect of Drug Testing: Evidence from the National Logitudinal Survey of Youth. Storming Media, 1999.

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47

Farquhar-Smith, Paul. A seminal paper on the epidemiology of cancer pain. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0063.

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The landmark paper discussed in this chapter is ‘Prevalence of pain in patients with cancer: A systematic review of the past 40 years’, published by van den Beuken et al. in 2007. It is not surprising that this definitive study on cancer pain prevalence is one of the most cited papers in cancer pain. Despite the extent of cancer pain literature, this paper’s 2007 publication is surprisingly recent for the first methodologically sound and major study of cancer pain prevalence. Many previous estimates lacked accuracy, and were prone to bias. What was known was that, despite apparent increasing i

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48

Klabunde, Anna. Computational Economic Modeling of Migration. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.41.

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In this chapter an agent-based model of endogenously evolving migrant networks is developed to find and estimate the size of determinants of migration and return decisions. Individuals are connected by links, the strength of which declines over time and distance. Methodologically speaking, this chapter combines parameterization using data from the Mexican Migration Project with calibration. It is shown that expected earnings, an idiosyncratic home bias, network ties to other migrants, strength of links to the home country, and age have a significant impact on circular migration patterns over t

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49

Li, Bin Grace, Christopher Adam, Andrew Berg, Peter Montiel, and Stephen O’Connell. Identifying the Monetary Transmission Mechanism in Sub-Saharan Africa. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198785811.003.0006.

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VAR methods suggest that the monetary transmission mechanism may be weak and unreliable in low-income countries. But are structural VARs identified via short-run restrictions capable of detecting a transmission mechanism where one exists, under research conditions typical of these countries? Using small DSGEs as data-generating processes, the chapter assesses the impact on VAR-based inference of short data samples, measurement error, high-frequency supply shocks, and other features of the LIC environment. The impact of these features on finite-sample bias appears to be relatively modest when i

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50

Morral, Andrew, Kristie Gore, and Terry Schell. Sexual Assault and Sexual Harassment in the U.S. Military: Volume 4. Investigations of Potential Bias in Estimates from the 2014 RAND Military Workplace Study. RAND Corporation, 2016. http://dx.doi.org/10.7249/rr870.6.

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