A bootstrap approximation for the distribution of the Local Whittle estimator

2019 ◽

Vol 12 ◽

Author(s):

Kiran Ahuja ◽

Brahmjit Singh ◽

Rajesh Khanna

Keyword(s):

Real Time ◽

Estimation Error ◽

Network Selection ◽

Bandwidth Estimation ◽

Heterogeneous Environment ◽

Bootstrap Approximation ◽

Available Bandwidth ◽

Internet Connection ◽

Available Bandwidth Estimation ◽

Selection Of

Background: With the availability of multiple options in wireless network simultaneously, Always Best Connected (ABC) requires dynamic selection of the best network and access technologies. Objective: In this paper, a novel dynamic access network selection algorithm based on the real time is proposed. The available bandwidth (ABW) of each network is required to be estimated to solve the network selection problem. Method: Proposed algorithm estimates available bandwidth by taking averages, peaks, low points and bootstrap approximation for network selection. It monitors real-time internet connection and resolves the selection issue in internet connection. The proposed algorithm is capable of adapting to prevailing network conditions in heterogeneous environment of 2G, 3G and WLAN networks without user intervention. It is implemented in temporal and spatial domains to check its robustness. Estimation error, overhead, estimation time with the varying size of traffic and reliability are used as the performance metrics. Results: Through numerical results, it is shown that the proposed algorithm’s ABW estimation based on bootstrap approximation gives improved performance in terms of estimation error (less than 20%), overhead (varies from 0.03% to 83%) and reliability (approx. 99%) with respect to existing techniques. Conclusion: Our proposed methodology of network selection criterion estimates the available bandwidth by taking averages, peaks, and low points and bootstrap approximation method (standard deviation) for the selection of network in the wireless heterogeneous environment. It monitors real-time internet connection and resolves internet connections selection issue. All the real-time usage and test results demonstrate the productivity and adequacy of available bandwidth estimation with bootstrap approximation as a practical solution for consistent correspondence among heterogeneous wireless networks by precise network selection for multimedia services.

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Consistency of bootstrap approximation to the null distributions of local spatial statistics with application to house price analysis

Journal of Inequalities and Applications ◽

10.1186/s13660-020-02482-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Chang-Lin Mei ◽

Shou-Fang Xu ◽

Feng Chen

Keyword(s):

Spatial Statistics ◽

Bootstrap Method ◽

Spatial Association ◽

House Price ◽

Housing Price ◽

Bootstrap Approximation ◽

Local Statistics ◽

Null Distributions ◽

Local Spatial Statistics ◽

Distribution Of A Statistic

Abstract With the increasing availability of spatially extensive geo-referenced data, much attention has been paid to the use of local statistics to identify local patterns of spatial association, in which the null distributions of local statistics play an essential role in the related statistical inference. As a powerful tool to approximate the distribution of a statistic, the bootstrap method is used in this paper to derive null distributions of the commonly used local spatial statistics including local Getis and Ord’s $G_{i}$ G i , Moran’s $I_{i}$ I i and Geary’s $c_{i}$ c i . Strong consistency of the bootstrap approximation to the null distributions of the statistics is proved under some mild conditions, and the Boston housing price data are analyzed to demonstrate the application of the theoretical results.

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Bootstrap Approximation to Prediction MSE for State-Space Models with Estimated Parameters

Journal of Time Series Analysis ◽

10.1111/j.1467-9892.2005.00448.x ◽

2005 ◽

Vol 26 (6) ◽

pp. 893-916 ◽

Cited By ~ 25

Author(s):

Danny Pfeffermann ◽

Richard Tiller

Keyword(s):

State Space ◽

State Space Models ◽

Bootstrap Approximation ◽

Estimated Parameters

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Monitoring Persistence Change in Heavy-Tailed Observations

Symmetry ◽

10.3390/sym13060936 ◽

2021 ◽

Vol 13 (6) ◽

pp. 936

Author(s):

Dan Wang

Keyword(s):

Kernel Method ◽

Alternative Hypothesis ◽

Null Distribution ◽

Real Data ◽

Ratio Test ◽

Finite Sample ◽

Test Statistic ◽

Bootstrap Approximation ◽

Heavy Tailed ◽

Better Than

In this paper, a ratio test based on bootstrap approximation is proposed to detect the persistence change in heavy-tailed observations. This paper focuses on the symmetry testing problems of I(1)-to-I(0) and I(0)-to-I(1). On the basis of residual CUSUM, the test statistic is constructed in a ratio form. I prove the null distribution of the test statistic. The consistency under alternative hypothesis is also discussed. However, the null distribution of the test statistic contains an unknown tail index. To address this challenge, I present a bootstrap approximation method for determining the rejection region of this test. Simulation studies of artificial data are conducted to assess the finite sample performance, which shows that our method is better than the kernel method in all listed cases. The analysis of real data also demonstrates the excellent performance of this method.

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