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Pseudoscore‐based estimation from biased observations
Statistics in Medicine, 2006AbstractThere are many practical situations where observation of the primary variableYfor individuals in a population is incomplete and depends on some auxiliary variablesXthat are potentially correlated withY. We consider parameter estimation for the distribution ofYwith the incomplete data, without specifying the underlying association betweenYandX ...
X Joan, Hu +3 more
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1983
Publisher Summary This chapter presents the conventional linear-statistical models, estimators, and hypothesis testing framework. Sampling theory and Bayes estimators that permit sample information and various types of nonsample information are specified and evaluated.
G.G. Judge, M.E. Bock
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Publisher Summary This chapter presents the conventional linear-statistical models, estimators, and hypothesis testing framework. Sampling theory and Bayes estimators that permit sample information and various types of nonsample information are specified and evaluated.
G.G. Judge, M.E. Bock
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Rethinking biased estimation [Lecture Notes]
IEEE Signal Processing Magazine, 2008In this lecture note we discuss methods to improve the accuracy of unbiased estimators used in many signal processing problems. Our approach is based on introducing a bias as a means of reducing the mean-squared error (MSE). The important aspect of our framework is that the reduction in MSE is guaranteed for all values of the unknown parameter.
S. Kay, Y.C. Eldar
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Accurate localization using biased estimation
2010 Sixth International Conference on Natural Computation, 2010Localization of mobile stations(MS) is of considerable interest in wireless communication. In this paper, three algorithms are developed for accurate mobile location using the time-of-arrival measurements. The first algorithm is an unconstrained least square estimator(LSE) which has implementation simplicity. Our methods referring linear bias estimator(
Xiao Zhang, Qun Wan
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ESTIMATION FROM A LENGTH-BIASED DISTRIBUTION
Statistics & Risk Modeling, 1985Let \(F_ n(t)\) be the non-parametric MLE of a life time distribution F on the basis of a sample of size n from the length biased distribution of F. If \(G(t)=\mu^{-1}\int^{t}_{0}x dF(x)\) and \(G_ n(t)\) denotes the empirical df then \[ F_ n(t)=\int^{t}_{0}y^{-1}dG_ n(y)/\int^{\infty}_{0}y^{-1}dG_ n(y). \] On the assumption that G is continuous on (0,\
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CHARACTERIZATION OF STATE ESTIMATION BIASES
Probability in the Engineering and Informational Sciences, 2005The control and operation of an electric power system is based on the ability to determine the state of the system in real time. State estimation (SE) has been introduced in the 1960s to achieve this objective. The initial implementation was based on single-phase measurements and a power system model that was assumed to operate under single-frequency ...
A. P. Sakis Meliopoulos +1 more
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Biases in CAPM Beta Estimation
SSRN Electronic Journal, 2016In this paper, we show that conditions derived under the CAPM ensure only weak exogeneity in a linear regression setting. Since strong exogeneity is not guaranteed, the OLS estimator of CAPM beta is only consistent but not necessarily unbiased. We provide empirical evidence that individual daily stock returns exibit regime-switching patterns and may ...
Linda H. Chen +3 more
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Biased Estimation in Regression
1979Abstract : Summarized in the report are four scholarly papers that have been published during the contract period and four other articles which are in various stages of submission for publication. Several other activities directly connected with this research are briefly discussed. A list of all research articles published since the commencement of the
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Estimation of Biased Technical Progress
1999Empirical work on neoclassical growth models led to the recognition that technological progress is the dominant factor in the growth of per capita income. This led the economic profession to explore four questions: (i) How important is technological and technical progress in the process of economic growth? (ii) What is the cause of technical progress -
Ryuzo Sato +2 more
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A biased estimate of proportion
International Journal of Mathematical Education in Science and Technology, 1998A simple method of finding a biased estimate of the population proportion is introduced. This biased estimate is derived by finding a confidence interval for the population proportion without using the concept of convergence in probability. While all existing biased estimates of the population proportion are based on specific prior distributions, the ...
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