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Coverage probability

About: Coverage probability is a research topic. Over the lifetime, 2479 publications have been published within this topic receiving 53259 citations.


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Journal ArticleDOI
TL;DR: In this article, the authors examined the small sample coverage probability and size of jackknife confidence intervals centered at a Stein-rule estimator, and used a Monte Carlo experiment to explore the coverage probabilities and lengths of nominal 90% and 95% delete-one and infinitesimal confidence intervals.
Abstract: The primary goal of this paper is to examine the small sample coverage probability and size of jackknife confidence intervals centered at a Stein-rule estimator. A Monte Carlo experiment is used to explore the coverage probabilities and lengths of nominal 90% and 95% delete-one and infinitesimal jackknife confidence intervals centered at the Stein-rule estimator; these are compared to those obtained using a bootstrap procedure.

14 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented the new confidence interval for the coefficient of variation of lognormal distribution with restricted parameter, and proved the coverage probability and expected length of their proposed confidence interval.
Abstract: This paper presents the new confidence interval for the coefficient of variation of lognormal distribution with restricted parameter. We proved the coverage probability and expected length of our proposed confidence interval.

14 citations

Proceedings ArticleDOI
02 May 2013
TL;DR: This paper studies coverage maximization for cellular networks in which base station (BS) locations are modeled using a homogenous spatial Poisson point process, and user locations are arbitrary, to derive general bounded path loss models satisfying some mild conditions.
Abstract: This paper studies the coverage maximization for wireless networks in which base station (BS) locations are drawn from a homogenous spatial Poisson point process, and user locations are arbitrary. A user is covered for communication if its received signal-to-noise-ratio (SNR) is above a given threshold value, and the objective is to maximize the coverage probability under per unit area power density constraints. The resulting optimization problem is solved analytically by making use of the underlying concavity in the objective function when transmissions are impaired only by a power-law bounded path loss. Our results show that the optimal transmit power per BS is independent of the power density constraint, and the solution to the optimization problem represents the Pareto optimal boundary between the power density constraint and the coverage probability. Then, these results are extended to a system in which transmissions are impaired by both path loss and fading. The resulting optimization problem with fading turns out to be a non-convex optimization problem. In this case, we provide tight upper bounds on the optimal coverage probability. The paper also discusses the importance of using bounded path loss models for coverage maximization problems in wireless networks, and shows that an unbounded model will lead to trivial solutions.

14 citations

Journal ArticleDOI
TL;DR: A new class of confidence sets for the mean of a p-variate normal distribution (p≥3) is introduced in this paper, which are neither spheres nor ellipsoids.
Abstract: A new class of confidence sets for the mean of a p-variate normal distribution (p≥3) is introduced They are neither spheres nor ellipsoids We show that we can construct our confidence sets so that their coverage probabilities are equal to the specified confidence coefficient Some of them are shown to dominate the usual confidence set, a sphere centered at the observations Numerical results are also given which show how small their volumes are

14 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigate the finite sample properties of the estimator of a persistence parameter of an unobservable common factor when the factor is estimated by the principal components method.
Abstract: We investigate the finite sample properties of the estimator of a persistence parameter of an unobservable common factor when the factor is estimated by the principal components method. When the number of cross-sectional observations is not sufficiently large, relative to the number of time series observations, the autoregressive coefficient estimator of a positively autocorrelated factor is biased downward and the bias becomes larger for a more persistent factor. Based on theoretical and simulation analyses, we show that bootstrap procedures are e¤ective in reducing the bias, and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of the coverage probability.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
202363
2022153
2021142
2020151
2019142