ETSI TS 102 250-6-2004 Speech Processing Transmission and Quality Aspects (STQ) QoS aspects for popular services in GSM and 3G networks Part 6 Post processing and statistical metho_1.pdf
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1、 ETSI TS 102 250-6 V1.2.1 (2004-10)Technical Specification Speech Processing, Transmission and Quality Aspects (STQ);QoS aspects for popular services in GSM and 3G networks;Part 6: Post processing and statistical methodsETSI ETSI TS 102 250-6 V1.2.1 (2004-10) 2 Reference RTS/STQ-00061m Keywords 3G,
2、GSM, network, QoS, service, speech ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N 348 623 562 00017 - NAF 742 C Association but non lucratif enregistre la Sous-Prfecture de Grasse (06) N 7803/88 Important notice Individual c
3、opies of the present document can be downloaded from: http:/www.etsi.org The present document may be made available in more than one electronic version or in print. In any case of existing or perceived difference in contents between such versions, the reference version is the Portable Document Forma
4、t (PDF). In case of dispute, the reference shall be the printing on ETSI printers of the PDF version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current st
5、atus of this and other ETSI documents is available at http:/portal.etsi.org/tb/status/status.asp If you find errors in the present document, please send your comment to one of the following services: http:/portal.etsi.org/chaircor/ETSI_support.asp Copyright Notification No part may be reproduced exc
6、ept as authorized by written permission. The copyright and the foregoing restriction extend to reproduction in all media. European Telecommunications Standards Institute 2004. All rights reserved. DECTTM, PLUGTESTSTM and UMTSTM are Trade Marks of ETSI registered for the benefit of its Members. TIPHO
7、NTMand the TIPHON logo are Trade Marks currently being registered by ETSI for the benefit of its Members. 3GPPTM is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational Partners. ETSI ETSI TS 102 250-6 V1.2.1 (2004-10) 3 Contents Intellectual Property Rights6
8、 Foreword.6 Introduction 7 1 Scope 8 2 References 8 3 Definitions, symbols and abbreviations .8 3.1 Definitions8 3.2 Symbols9 3.3 Abbreviations .9 4 Important measurement data types in mobile communications .9 4.1 Data with binary values10 4.2 Data out of time-interval measurements.10 4.3 Measuremen
9、t of data throughput10 4.4 Data concerning quality measures10 5 Distributions and moments.11 5.1 Introduction 11 5.2 Continuous and discrete distributions.12 5.3 Definition of density function and distribution function 12 5.3.1 Probability Distribution Function (PDF) 12 5.3.2 Cumulative Distribution
10、 Function (CDF) .13 5.4 Moments and quantiles.13 5.5 Estimation of moments and quantiles.15 5.6 Important distributions .16 5.6.1 Continuous distributions .16 5.6.1.1 Normal distribution .16 5.6.1.1.1 Standard normal distribution .17 5.6.1.1.2 Central limit theorem.18 5.6.1.1.3 Transformation to nor
11、mality18 5.6.1.2 Log-Normal distribution .18 5.6.1.2.1 Use-case: transformations19 5.6.1.3 Exponential distribution19 5.6.1.4 Weibull distribution 20 5.6.1.5 Pareto distribution .21 5.6.1.6 Extreme distribution (Fisher-Tippett distribution) 22 5.6.2 Testing distributions .22 5.6.2.1 Chi-Square distr
12、ibution with n degrees of freedom 23 5.6.2.1.1 Further relations.24 5.6.2.1.2 Relation to empirical variance.24 5.6.2.2 Student t-distribution.24 5.6.2.2.1 Relation to normal distribution25 5.6.2.3 F distribution.26 5.6.2.3.1 Quantiles27 5.6.2.3.2 Approximation of quantiles .27 5.6.2.3.3 Relations t
13、o other distributions28 5.6.3 Discrete distributions 28 5.6.3.1 Bernoulli distribution 28 5.6.3.2 Binomial distribution 29 5.6.3.3 Geometric distribution 30 5.6.3.4 Poisson distribution.31 5.6.4 Transitions between distributions and appropriate approximations32 5.6.4.1 From binomial to Poisson distr
14、ibution 32 5.6.4.2 From binomial to Normal distribution 32 5.6.4.3 From Poisson to Normal distribution 32 ETSI ETSI TS 102 250-6 V1.2.1 (2004-10) 4 5.6.5 Truncated distributions .33 5.6.6 Distribution selection and parameter estimation.33 5.6.6.1 Test procedures .33 5.6.6.1.1 Chi-Square test 33 5.6.
15、6.1.2 Kolmogorov-Smirnov test .33 5.6.6.1.3 Shapiro-Wilk test.33 5.6.6.2 Parameter estimation methods 34 5.7 Evaluation of measurement data 34 5.7.1 Statistical tests 34 5.7.1.1 Formulation of statistical tests.34 5.7.1.2 Classes of statistical tests 35 5.7.1.3 Tests for normal and binomial data.35
16、5.7.1.3.1 One-sample tests for normal data 35 5.7.1.3.2 Two-sample tests for normal data36 5.7.1.3.3 Test for binomial data37 5.7.1.4 Distribution-free tests for location 38 5.7.1.4.1 Sign tests38 5.7.1.4.2 Sign rank test .38 5.7.1.4.3 Wilcoxon rank sum test .39 5.7.2 Confidence interval.39 5.7.2.1
17、Binomial distribution 40 5.7.2.2 Normal (Gaussian) distribution.41 5.7.3 Required sample size for certain confidence levels 42 6 Visualization techniques.43 6.1 Visualization of static data .43 6.1.1 Histograms43 6.1.2 Barplots.43 6.1.3 QQ-Plots .44 6.1.4 Boxplots44 6.2 Visualization of dynamic data
18、 45 6.2.1 Line Diagrams 45 6.2.2 Temporal changing Boxplots45 6.2.3 MMQ-Plots.46 7 Time series modelling 46 7.1 Descriptive characterization .47 7.1.1 Empirical moments.48 7.1.2 Decomposition of time series49 7.1.3 Determination of the trend component .50 7.1.3.1 Trend function types .51 7.1.3.1.1 L
19、inear trend function .51 7.1.3.1.2 Polynomial trend function .51 7.1.3.1.3 Non-linear trend models 52 7.1.3.2 Trend estimation .52 7.1.3.3 Transformation of time series by filtering.53 7.1.3.3.1 Linear filters 54 7.1.3.3.2 Exponential filters .56 7.1.4 Seasonal component .57 8 Data aggregation 58 8.
20、1 Basic data aggregation operators58 8.2 Data sources, structures and properties 59 8.2.1 Raw data .59 8.2.1.1 Performance data.59 8.2.1.2 Event data59 8.2.2 Key Performance Indicators / Parameters.60 8.3 Aggregation hierarchies .60 8.3.1 Temporal aggregation.60 8.3.2 Spatial aggregation .61 8.4 Par
21、ameter estimation methods61 8.4.1 Projection method.61 8.4.2 Substitution method 61 ETSI ETSI TS 102 250-6 V1.2.1 (2004-10) 5 8.4.3 Application of estimation methods .62 8.4.4 Attributes of aggregation operators 62 8.5 Weighted aggregation.63 8.5.1 Perceived QoS 63 8.5.2 Weighted quantiles .64 8.6 A
22、dditional data aggregation operators65 8.6.1 MAWD and BH65 8.6.2 AVGn65 9 Assessment of performance indices .65 9.1 Estimation of performance parameters based on active service probing systems 65 9.2 Monitoring concepts.65 9.2.1 Control charts66 9.2.1.1 Shewhart control charts.66 9.2.1.2 CUSUM and E
23、WMA charts66 9.2.2 Other alarming rules .66 9.3 Methods for evaluation of objectives .66 9.3.1 Desirability functions67 9.3.2 Loss functions.67 Annex A (informative): Examples of statistical calculations 68 A.1 Confidence intervals for binomial distribution.68 A.1.1 Step by step computation .68 A.1.
24、2 Computation using statistical software.70 A.1.2.1 Computation in R70 A.1.2.2 Computation in Excel .71 A.2 Transition from binomial to normal distribution71 A.3 Definitions of EG 201 769 .72 A.4 Calculation of confidence intervals73 A.4.1 Estimated rate 5 %73 A.4.2 Estimated rate 50 %74 A.4.3 Estim
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