SINGLE SAMPLING PLAN FOR VARIABLES UNDER MEASUREMENT ERROR FOR NON-NORMAL DISTRIBUTION

Similar documents
Statistics and Probability Letters. Variance stabilizing transformations of Poisson, binomial and negative binomial distributions

Non-Inferiority Tests for the Ratio of Two Correlated Proportions

ON JARQUE-BERA TESTS FOR ASSESSING MULTIVARIATE NORMALITY

Confidence Intervals for a Proportion Using Inverse Sampling when the Data is Subject to False-positive Misclassification

Sampling Procedure for Performance-Based Road Maintenance Evaluations

Objectives. 3.3 Toward statistical inference

A Comparative Study of Various Loss Functions in the Economic Tolerance Design

Objectives. 5.2, 8.1 Inference for a single proportion. Categorical data from a simple random sample. Binomial distribution

Feasibilitystudyofconstruction investmentprojectsassessment withregardtoriskandprobability

Supplemental Material: Buyer-Optimal Learning and Monopoly Pricing

Information and uncertainty in a queueing system

2/20/2013. of Manchester. The University COMP Building a yes / no classifier

Lecture 2. Main Topics: (Part II) Chapter 2 (2-7), Chapter 3. Bayes Theorem: Let A, B be two events, then. The probabilities P ( B), probability of B.

CONSUMER CREDIT SCHEME OF PRIVATE COMMERCIAL BANKS: CONSUMERS PREFERENCE AND FEEDBACK

Online Robustness Appendix to Are Household Surveys Like Tax Forms: Evidence from the Self Employed

Annex 4 - Poverty Predictors: Estimation and Algorithm for Computing Predicted Welfare Function

The Supply and Demand for Exports of Pakistan: The Polynomial Distributed Lag Model (PDL) Approach

Capital Budgeting: The Valuation of Unusual, Irregular, or Extraordinary Cash Flows

***SECTION 7.1*** Discrete and Continuous Random Variables

Re-testing liquidity constraints in 10 Asian developing countries

Effects of Size and Allocation Method on Stock Portfolio Performance: A Simulation Study

The Relationship Between the Adjusting Earnings Per Share and the Market Quality Indexes of the Listed Company 1

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

The Skewed Generalized T Distribution Tree Package Vignette

A NOTE ON SKEW-NORMAL DISTRIBUTION APPROXIMATION TO THE NEGATIVE BINOMAL DISTRIBUTION

1 < = α σ +σ < 0. Using the parameters and h = 1/365 this is N ( ) = If we use h = 1/252, the value would be N ( ) =

Revisiting the risk-return relation in the South African stock market

Quantitative Aggregate Effects of Asymmetric Information

Asymmetric Information

Statistical inferences and applications of the half exponential power distribution

Towards an advanced estimation of Measurement Uncertainty using Monte-Carlo Methods- case study kinematic TLS Observation Process

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

508-B (Statistics Camp, Wash U, Summer 2016) Asymptotics. Author: Andrés Hincapié and Linyi Cao. This Version: August 9, 2016

and their probabilities p

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November ISSN

Appendix Large Homogeneous Portfolio Approximation

Management Accounting of Production Overheads by Groups of Equipment

Technical Note: An Improved Range Chart for Normal and Long-Tailed Symmetrical Distributions

Policyholder Outcome Death Disability Neither Payout, x 10,000 5, ,000

Maximize the Sharpe Ratio and Minimize a VaR 1

Inventory Systems with Stochastic Demand and Supply: Properties and Approximations

A GENERALISED PRICE-SCORING MODEL FOR TENDER EVALUATION

SUBORDINATION BY ORTHOGONAL MARTINGALES IN L p, 1 < p Introduction: Orthogonal martingales and the Beurling-Ahlfors transform

Monetary policy is a controversial

Forward Vertical Integration: The Fixed-Proportion Case Revisited. Abstract

Oliver Hinz. Il-Horn Hann

Modeling and Estimating a Higher Systematic Co-Moment Asset Pricing Model in the Brazilian Stock Market. Autoria: Andre Luiz Carvalhal da Silva

8.1 Estimation of the Mean and Proportion

EVIDENCE OF ADVERSE SELECTION IN CROP INSURANCE MARKETS

The Two-Sample Independent Sample t Test

5.1 Regional investment attractiveness in an unstable and risky environment

Weighted Country Product Dummy Variable Regressions and Index Number Formulae

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS019) p.4301

Stochastic modelling of skewed data exhibiting long range dependence

The Impact of Flexibility And Capacity Allocation On The Performance of Primary Care Practices

Causal Links between Foreign Direct Investment and Economic Growth in Egypt

Sharpe Ratios and Alphas in Continuous Time

We connect the mix-flexibility and dual-sourcing literatures by studying unreliable supply chains that produce

INDEX NUMBERS. Introduction

A Multi-Objective Approach to Portfolio Optimization

Supply chain disruption assessment based on the newsvendor model

Statistical Tables Compiled by Alan J. Terry

Sample Size for Assessing Agreement between Two Methods of Measurement by Bland Altman Method

Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

Explanation on how to use to develop a sample plan or Answer question: How many samples should I take to ensure confidence in my data?

DEVELOPMENT AND PROJECTION OF SINGLE-PARENT FAMILIES IN THE CZECH REPUBLIC

Publication Efficiency at DSI FEM CULS An Application of the Data Envelopment Analysis

Are capital expenditures, R&D, advertisements and acquisitions positive NPV?

Asian Economic and Financial Review A MODEL FOR ESTIMATING THE DISTRIBUTION OF FUTURE POPULATION. Ben David Nissim.

Prediction of Rural Residents Consumption Expenditure Based on Lasso and Adaptive Lasso Methods

Lecture 5: Performance Analysis (part 1)

Stock Market Risk Premiums, Business Confidence and Consumer Confidence: Dynamic Effects and Variance Decomposition

Long Run Relationship between Capital Market and Banking Sector-A Cointegration on Federal Bank

Summary of the Chief Features of Alternative Asset Pricing Theories

Setting the regulatory WACC using Simulation and Loss Functions The case for standardising procedures

Price Gap and Welfare

FORECASTING EARNINGS PER SHARE FOR COMPANIES IN IT SECTOR USING MARKOV PROCESS MODEL

Utility and the Skewness of Return in Gambling

Brownian Motion, the Gaussian Lévy Process

Pricing CDOs with the Fourier Transform Method. Chien-Han Tseng Department of Finance National Taiwan University

A Convenient Way of Generating Normal Random Variables Using Generalized Exponential Distribution

External Debt and External Rate of Interest: An Empirical Analysis of Pakistan

A Semi-parametric Test for Drift Speci cation in the Di usion Model

SAMPLE STANDARD DEVIATION(s) CHART UNDER THE ASSUMPTION OF MODERATENESS AND ITS PERFORMANCE ANALYSIS

A Variance Estimator for Cohen s Kappa under a Clustered Sampling Design THESIS

Foreign direct investment in Fiji

Effects of Macroeconomic Volatility on Stock Prices in Kenya: A Cointegration Evidence from the Nairobi Securities Exchange (NSE)

Application of Sarima Models in Modelling and Forecasting Nigeria s Inflation Rates

Midterm Exam: Tuesday 28 March in class Sample exam problems ( Homework 5 ) available tomorrow at the latest

Physical and Financial Virtual Power Plants

A random variable X is a function that assigns (real) numbers to the elements of the sample space S of a random experiment.

TESTING THE CAPITAL ASSET PRICING MODEL AFTER CURRENCY REFORM: THE CASE OF ZIMBABWE STOCK EXCHANGE

Index Methodology Guidelines relating to the. EQM Global Cannabis Index

Management of Pricing Policies and Financial Risk as a Key Element for Short Term Scheduling Optimization

A new class of Bayesian semi-parametric models with applications to option pricing

COMPARISON OF RATIO ESTIMATORS WITH TWO AUXILIARY VARIABLES K. RANGA RAO. College of Dairy Technology, SPVNR TSU VAFS, Kamareddy, Telangana, India

Does Anti-dumping Enforcement Generate Threat?

Matching Markets and Social Networks

Comparing the Means of. Two Log-Normal Distributions: A Likelihood Approach

Available online at International Journal of Current Research Vol. 8, Issue, 12, pp , December, 2016

Transcription:

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, SINGLE SAMPLING PLAN FOR VARIABLES UNDER MEASUREMENT ERROR FOR NON-NORMAL DISTRIBUTION Dr. ketki kulkarni Jayee University of Engineering and Technology Guna (M.P.) ketki@rediffmail.com Abstract In this aer, the single samling lan for variables under measurement error for non-normal distributions reresented by the first four terms of an Edgeworth series is studied for known case. The effect of using the normal theory samling lan in a non-normal situation is studied by obtaining the distorted errors of the first and second kind. As one will be interested in having a suitable samling lan under measurement error for non-normal variables, the values of n and k are determined. Keywords: Non-Normal distribution, Edgeworth series, Measurement error, Single Samling Plan. Introduction The most imortant area for studying the effect of error due to measurement and misclassification is the samling insection lan. In general, imbedded within the design of accetance samling lans is the assumtion that insection rocedure are free from error and normal distribution. In the area of samling insection lan by variables very little attention has aid so for to investigate the effect of measurement error. Walsh (96) studied the effect of measurement error on the characteristic function of a single samling insection by variables with one-sided secification limit. The assumtion made by them to investigate the effect of measurement error are (i) the error is unbiased and indeendent of the actual value of the characteristics measured (ii) both the true value of the characteristic measured and the measurement error follows normal distributions (iii) the ratio of the oulation variance to the error variance has a ositive uer bound. Singh (966) studied the effect of measurement on the oerating characteristic function of a single samling by variables for normal as well non-normal roduct distributions assuming one-sided secification limit, viz, the uer secification limit. His assumtions were almost same as those of Walsh (96). Bennett et al. (97) investigated the effect of Tye-I and Tye-II insection errors on the economic design of a single samling lan by attributes. Case et al. (975) develoed the formula for evaluating the average outgoing quality function when attributes examination is subject to Tye-I and Tye-II insection errors. The following hyerbolic remarks of Geary (97b) strike out: "Normality is a myth, there never was, and never will be normal distribution". Now the question arises whether such otimistic assumtion of normality is likely to be seriously misleading under non-normal situations. In other words, to what extent the standard statistical rocedures develoed for normal universes are alicable, when samles, in real sense, come from other than normal oulations. A statistical rocedure which is insensitive to deartures from the assumtions, which underlie it is called "Robust" an at term introduced by Box (95) and now widely used. An early survey of the literature on non-normality and robustness was given by Box and Anderson (955). In recent years, distribution-free or non-arametric methods have become quite oular because they are readily comutable and ermit freedom from worry about the classical assumtions of the standard normal theory. It should, however, be ointed out that in cases where classical assumtions hold entirely or even aroximately, the analogous standard normal rocedures are generally more efficient for detecting deartures from the null hyothesis..samling Distribution of Observed Mean Assuming that the true measurement x and the random error of measurement e are additive, we can write the observed measurement as : 5

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, = x + e, (.) where x and e are indeendent. where, We now assume the density function of x to be secified by the first four terms of the Edgeworth series as follows: xµ () xµ () xµ (6) xµ f ( x) = + +, (.) 6 7 d ( t) = ex ( t ) r π dt r r [ t / ] and = (t). Then the density function of can be written from equation (.) as f () = µ ρ 6 (6) µ, 7 () µ +ρ 6 + ρ (.) which is again an Edgeworth series. () µ The distribution of for the observed samles of size n drawn from the oulation (.) is found by following Gayen (95) as, f () = n µ ρ / n 6 n () µ +ρ / n n () µ / n (6) µ, 7n / n 6 + ρ (.) where r= =. e ρ (ρ ) We now examine the effect of the measurement error on the usual test criterion of single samling lan described below: Accet the lot if x+ k U, And reject otherwise, for a given set of values of the roducer's risk, consumer's risk, Accetable Quality Level (AQL) and Lot Tolerance Proortion Defective (LTPD), the values of n and k are determined by the formulae 6

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, where (K K ) n (K K ) α + β = (KαK k= (K α + K β, + K K β ) ) K, K K and K are determined by the equation (.5) (.6) kθ ex π t dt=θ, (.7) for different choices of fraction defective θ. If θ is the roortion defective in the lot, we know that U µ = K θ. The OC function L corresonding to a fraction defective is found out as follows. Under the assumtion of normality, a lot having ercent defective items will be acceted, if x + k U =µ+ K (.8) where K is given by equation (.8) for θ =. The exression for robability of accetance under measurement error L = Pr ob. [x+ k µ+ K ] k is derived by recalling the normality of the statistic ( x+ ). The above robability after some simlification works out to be L = Φ[ n ρ (K k)], (.9) where Φ(t) = t π ex x dx.. Known Plan Under Measurement Error for Non-Normal Situation Let the true quality characteristic x follows the first four term of an Edgeworth series under measurement error. The OC function of the lan is given by L' = Pr ob [ x+ k U = µ = µ '], where ' = U K', µ 7

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, K' being the uer ercent oint of the standardized Edgeworthian oulation. i.e., K' is given by K ' () () (6) ( x) ( x) + ( x) + ( x) dx=. 6 7 (.) µ (µ) = ρ / n where x=, x, (x) e. = Using the distribution of x from equation (.), the OC function of the known lan is obtained as L' π () () 6 (5) = Φ( z) ρ ( z) + ρ ( z) + ρ ( ). 6 n n 7n z (.) where z = n ρ K ' k ). and ( The equation for determining the value of the lan arameters n and k are x L '( ) = α, (.) L ( ) = β. (.) ' Exlicit exression for n and k cannot be obtained under measurement error and non-normal situations. Equation (.) and (.) can however, be solved numerically. If n o and k o are the initial solutions than the imroved solution can be obtained as (n +δ n ) and (k +δ k ) where δ n and δk are the solution of linear equations A ( ) δ n B( ) δk = α c( ) (.5) and A( ) δ n B( ) δk =β c( ), (.6) where A() ρ = n [z Φ(z ) ρ (z (z ) (z )) + () () 6 n n () () 6 (6) (5) z (z ) (z )) + ρ (z (z ) (z )] (.7) 7n ( () () () = n ρ Φ(z ) ρ (z ) + ρ (z ) 6 n n B ρ 8

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, (6) + ρ (z ) 7n () () C() = Φ(z ) ρ (z ) + ρ (z ) 6 n n (.8) 6 (5) + ρ (z ) (.9) 7n and z = n ρ K ' ). ( k The required value of n and k can be obtained by taking the normal theory values as the initial solution and reeating the rocess of interaction for equation (.5) and (.6) till the desired accuracy is obtained..discussion of Numerical Results and Conclusions For the urose of illustrating the effect of measurement error and non- normality on the error of the first and second kind and the lan arameters n and k, we have determined the values of these quantities for few chosen sets of values of,, α β,, and for different error sizes. The values of n and k are determined from the equations (.5) and (.6). The actual error of the first and second kind are given by α = L'( ) and β' L' ( ). ' = The actual errors of the first kind when normal theory known lan is used under measurement error and non-normal situations are determined and resented in the Table (). As is evident from the Table, for letokurtic, latykurtic oulation under different error sizes the marked difference is seen in α' and β'. The effect of skewness, kurtosis and measurement error are serious. For few non-normal situations and measurement error the value of the lan arameters n and k are given in Table (). The values of n are rounded u and the values of k are given u to decimal laces which is correct u to the third lace of decimal. As can be seen from the Table the negative skewness and negative kurtosis increases n where as the ositive skewness and ositive kurtosis decreases the value of n. The value of k is also considerably affected by measurement error and the non-normality of the oulation. It may inferred that the use of normal theory samling lan is not valid for the non-normal situations and measurement error roblems. Even when there is slight dearture from normality and different error sizes it is advisable to take into account the measurement error and the non-normality of the arent oulation while choosing the samling lan arameters n and k. References ENNETT, G.K., CASE, K.E. and SCHMIDT, J.W. (97). The economic effects of insection error on attribute samling lans noval research logistics quarterly. Vol., No., -. BO, G.E.P. (95). Non-Normality and tests of variances. Biometrika,, 8-5. 9

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, BO, G.E.P. and ANDERSON, S.L. (955). Permutation theory in the derivation of Robustness criteria and the study of deartures from assumtions, JRSS, B, 7, -. CASE, K.E., BENNETT, G.K. and SCHMIDT, J.W. (975). The effect of insection error on average outgoing quality journal of quality technology, vol. 7, No. -. GAYEN, A.K. (95). The Inverse Hyerbolic sine Transformation on Student's - t for non-normal, samles, Sankhya,, 5-8. GEARY R.C. (97b). Testing for normality, biomatrika,,. 9-. SINGH, H.R. (966). Test and Measurement Error in statistical quality control, unublished, Ph.D. Thesis, IIT, Kharagur. WALSH, J.E. (96). Loss in test efficiency due to disclassification into Tables. Biometrika, 9, 58.

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, Table Value of α' and β' (Underline) for known lan α =.5, β =., =.5, =. / r = r = r = 6 r =.5.5.5.5..695.9.57.9.56.7.969.58....867..8..8 -.5.659.7.57.7.86.5.7.8.69.7.8.98..9..9.5.695.98.57.9.56.7.85.58..9..85..87.97.8.5.778.58.6.9.585..56..87.7.78.59.668.558.6.58.5.85.9.67.86.66.67.6.9.67.576.5..9.5.59.9

ISSN -58 (Paer) ISSN 5-5 (Online) Vol., No.9, Table Values of n (integer) and k for known lan α =.5, β=., =.5, =. / r = r = r = 6 r =.5.5.5.5. 7 6 8 7 7 8 7 6.9.96...98.979.5.97 -.5 7 6 9 8 8 7 8 7.68.999.786.5.5..9..5 7 6 8 7 7 6 7 6.98.956.55..6.975.6.967.5 5 5 8 5 5 5.97.98.9759.96.97.89.969.87.5 5.8.77.9.865.887.76.865.79