Authenticating ANN-NAR and ANN-NARMA Models Utilizing Bootstrap Techniques

2017 ◽  
pp. 761-771
Author(s):  
Nor Azura Md. Ghani ◽  
Saadi bin Ahmad Kamaruddin ◽  
Norazan Mohamed Ramli ◽  
Ali Selamat
Keyword(s):  
Bootstrap Techniques

scholarly journals The 3d $$ \mathcal{N} $$ = 6 bootstrap: from higher spins to strings to membranes

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Damon J. Binder ◽  
Shai M. Chester ◽  
Max Jerdee ◽  
Silviu S. Pufu
Keyword(s):  
Block Decomposition ◽  
Point Function ◽  
Present Evidence ◽  
Bootstrap Techniques ◽  
Wide Range ◽  
Higher Spin ◽  
Tensor Multiplet ◽  
Higher Spins ◽  
M Theory ◽  
Chern Simons

Abstract We study the space of 3d $$ \mathcal{N} $$ N = 6 SCFTs by combining numerical bootstrap techniques with exact results derived using supersymmetric localization. First we derive the superconformal block decomposition of the four-point function of the stress tensor multiplet superconformal primary. We then use supersymmetric localization results for the $$ \mathcal{N} $$ N = 6 U(N)k × U(N + M)−k Chern-Simons-matter theories to determine two protected OPE coefficients for many values of N, M, k. These two exact inputs are combined with the numerical bootstrap to compute precise rigorous islands for a wide range of N, k at M = 0, so that we can non-perturbatively interpolate between SCFTs with M-theory duals at small k and string theory duals at large k. We also present evidence that the localization results for the U(1)2M × U (1 + M)−2M theory, which has a vector-like large-M limit dual to higher spin theory, saturates the bootstrap bounds for certain protected CFT data. The extremal functional allows us to then conjecturally reconstruct low-lying CFT data for this theory.

scholarly journals Double Kernel Method Using Line Transect Sampling

2016 ◽  
Vol 41 (2) ◽  
Author(s):  
Omar Eidous ◽  
M.K. Shakhatreh
Keyword(s):  
Kernel Method ◽  
Kernel Estimator ◽  
Real Data ◽  
Line Transect ◽  
Population Abundance ◽  
Line Transect Sampling ◽  
Numerical Examples ◽  
Bootstrap Techniques ◽  
Transect Sampling ◽  
Kernel Type

A double kernel method as an alternative to the classical kernel method is proposed to estimate the population abundance by using line transect sampling. The proposed method produces an estimator that is essentially a kernel type of estimator use the kernel estimator twice to improve the performances of the classical kernel estimator. The feasibility of using bootstrap techniques to estimate the bias and variance of the proposed estimator is also addressed. Some numerical examples based on simulated and real data are presented. The results show that the proposed estimator outperforms existingclassical kernel estimator in most considered cases.

Efficiency analysis of viticulture systems in the Portuguese Douro region

2020 ◽  
Vol 32 (4) ◽  
pp. 573-591
Author(s):  
Micael Queiroga dos Santos ◽  
Xosé A. Rodríguez ◽  
Ana Marta-Costa
Keyword(s):  
Design Methodology ◽  
Production Systems ◽  
Efficiency Analysis ◽  
Data Envelopment ◽  
Content Type ◽  
Face To Face ◽  
Bootstrap Techniques ◽  
Specific Input ◽  
Low Efficiency ◽  
Output Information

Purpose The purpose of this paper is to estimate and analyse the technical efficiency (TE) component of productivity for a sample of winegrowers from the Douro Demarcated Region in Portugal. Design/methodology/approach The data were collected through face-to-face surveys and includes a sample of 110 farmers’ vineyards with specific input-output information and other data about production systems during the year of 2017. The authors use a two-stage data envelopment analysis using bootstrap techniques to obtain TE scores in the farmers’ vineyards and to examine the determinants of its efficiency. Findings The results show that some farmers’ vineyards have a low efficiency level and that there are essential determinants of the production system, which can influence its efficiency. This suggests considerable opportunities for improvement of wine grape productivity through better use of available resources considering the state of technology. Originality/value This work has overcome the lack of data in the farmers’ vineyards, the lack of efficiency studies in the region and also allowed to evaluate the production systems and to assess their impact on efficiency.

Confidence regions for INDSCAL using the jackknife and bootstrap techniques

Psychometrika ◽  
1984 ◽  
Vol 49 (4) ◽  
pp. 475-491 ◽  
Author(s):  
Sharon L. Weinberg ◽  
J. Douglas Carroll ◽  
Harvey S. Cohen
Keyword(s):  
Confidence Regions ◽  
Bootstrap Techniques

scholarly journals SLACK AND NET TECHNICAL EFFICIENCY MEASUREMENT: A BOOTSTRAP APPROACH

2005 ◽  
Vol 04 (03) ◽  
pp. 395-410 ◽  
Author(s):  
J. RICHMOND
Keyword(s):  
Confidence Intervals ◽  
Technical Efficiency ◽  
Small Component ◽  
Empirical Application ◽  
Use Of Resources ◽  
Bootstrap Techniques ◽  
Bootstrap Approach ◽  
Efficiency Estimation ◽  
Downward Bias ◽  
Cautious Interpretation

Statistical properties of DEA methods for efficiency estimation are poorly understood and currently the best way forward must be to use bootstrap techniques. The article seeks to extend bootstrap methods to allow investigation of the properties of estimates of inefficiencies due to the slack in the use of resources as well as technical efficiency. In an empirical application, it is found that inefficiency due to slack is a small component of the overall inefficiency and that the DEA technical efficiency estimates have a small downward bias, with confidence intervals that are wide enough to suggest cautious interpretation.

scholarly journals Efficient Bootstrap Estimates for Tail Statistics

2016 ◽  
Author(s):  
Øyvind Breivik ◽  
Ole Johan Aarnes
Keyword(s):  
Confidence Intervals ◽  
Temporal Resolution ◽  
Small Subset ◽  
High Temporal Resolution ◽  
Data Set ◽  
Empirical Distributions ◽  
Bootstrap Techniques ◽  
Entire Sequence ◽  
Bootstrap Estimates ◽  
Return Value

Abstract. Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates based on the extremal behaviour of the distribution. Specifically, the confidence intervals on return value estimates or bounds on in-sample tail statistics can be estimated using bootstrap techniques. However, bootstrapping from the entire data set is expensive. It is shown here that it suffices to bootstrap from a small subset consisting of the highest entries in the sequence to make estimates that are essentially identical to bootstraps from the entire sequence. Similarly, bootstrap estimates of confidence intervals of threshold return estimates are found to be well approximated by using a subset consisting of the highest entries. This has practical consequences in fields such as meteorology, oceanography and hydrology where return estimates are routinely made from very large gridded model integrations spanning decades at high temporal resolution. In such cases the computational savings are substantial.

Sign in / Sign up

Export Citation Format

Share Document