Results 191 to 200 of about 13,308 (240)
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Applied Mathematics and Computation, 1991
This paper is concerned with convergence acceleration of the jackknife. Recognizing the similarity of the ratios of determinants involved in the E-algorithm, a convergence acceleration algorithm, and the jackknife, the author shows how the E-algorithm can be used for implementing the jackknife.
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This paper is concerned with convergence acceleration of the jackknife. Recognizing the similarity of the ratios of determinants involved in the E-algorithm, a convergence acceleration algorithm, and the jackknife, the author shows how the E-algorithm can be used for implementing the jackknife.
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1995
This chapter presents theory for the jackknife in the case where the data are i.i.d. Many results can be extended in a straightforward manner to more complicated cases, which will be studied in later chapters. We begin this chapter by first focusing on jackknife variance estimators.
Jun Shao, Dongsheng Tu
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This chapter presents theory for the jackknife in the case where the data are i.i.d. Many results can be extended in a straightforward manner to more complicated cases, which will be studied in later chapters. We begin this chapter by first focusing on jackknife variance estimators.
Jun Shao, Dongsheng Tu
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2010
This chapter deals with statistical methods that, in some way, avoid mathematical difficulties that one would be facing using traditional approaches. The traditional approach of mathematical statistics is based on analytic expressions, or formulas, so avoiding these might seem itself a formidable task, especially in view of the chapters that so far ...
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This chapter deals with statistical methods that, in some way, avoid mathematical difficulties that one would be facing using traditional approaches. The traditional approach of mathematical statistics is based on analytic expressions, or formulas, so avoiding these might seem itself a formidable task, especially in view of the chapters that so far ...
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The Generalized Jackknife Statistic
International Statistical Review / Revue Internationale de Statistique, 1974David S. Salsburg +2 more
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Revue de l'Institut International de Statistique / Review of the International Statistical Institute, 1971
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Jackknife resampling parameter estimation method for weighted total least squares
Communications in Statistics - Theory and Methods, 2020Leyang Wang
exaly
Calculating Jackknife Variance Estimators for Parameters of the Gini Method
Journal of Business and Economic Statistics, 1991Shlomo Yitzhaki
exaly
Revue d'epidemiologie et de sante publique, 1992
The jackknife and the bootstrap are two non parametric methods which provide estimates- of the bias and the variance of an estimator, without any assumption about its statistical distribution. The jackknife is based on the observation of the estimator for subsamples, generally of size n-1, obtained from the original sample.
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The jackknife and the bootstrap are two non parametric methods which provide estimates- of the bias and the variance of an estimator, without any assumption about its statistical distribution. The jackknife is based on the observation of the estimator for subsamples, generally of size n-1, obtained from the original sample.
openaire +1 more source