joint monitoring of process mean and variance using exponentially weighted moving average control charts the performances of control charting schemes for thejoint monitoring of a process mean and a variance are evaluated and compared certain schemes are shown to be inadequate for this purpose and the potential risk of using these schemes alone is discussed a combined scheme consisting of a two sided exponentially weighted moving average ewma mean chart and a two sided ewma variance chart is found to perform well under several types of out of control situations a simple method is provided for approximating the average run length and the percentage points of a run length distribution of a combined ewma scheme a simple procedure is also proposed for the design of a combined ewma scheme

some omnibus exponentially weighted moving average statistical process monitoring schemes exponentially weighted moving average ewma quality monitoring schemes capahlc of detecting changes in both location and spread referred to as omnibus ewma s are proposed omnibus ewma s are based on the exponentiation of the absolute value of the standardized sample mean of the observations the process target mean and standard deviation are used for standardizing design procedures and considerations including a fast initial response feature are discussed average run lengths for exponents of 5 and 2 and various values of the weighting constant are presented the proposed schemes are compared with each other and with a number of selected well known omnibus schemes and a proposed omnibus cumulative sum scheme schemes with different sample sizes are used and off target models include both static and dynamic alternatives comparisons are based on average number of observations to detection and expected loss due to poor quality one of the omnibus ewma schemes is shown to be best with regard to exp

a reference free cuscore chart for dynamic mean change detection and a unified framework for charting performance comparison to detect and estimate nonconstant time varying mean shifts statistical process control spc tools such as the cumulative score cuscore and generalized likelihood ratio test glrt charts have recently been proposed however their efficiency is based on previous and exact knowledge of a reference pattern in this article a reference free cuscore rfcuscore chart is proposed that can trace and detect dynamic mean changes quickly without knowing the reference pattern in addition a unified framework that contains most of the control charts is presented and applied for a theoretical comparison of the rfcuscore cuscore glrt and cusum charts in detecting dynamic mean changes moreover numerical simulations and a real example are used to illustrate and verify the results both theoretical analysis and numerical results show that the rfcuscore chart performs not only robustly but also quickly in detecting both small and large dynamic mean changes

on the second greedy weight for linear codes of dimension at least 4 the maximum of g sub 2 d sub 2 for linear n k d q codes c is studied here d sub 2 is the smallest size of the support of a two dimensional subcode of c and g sub 2 is the smallest size of the support of a two dimensional subcode of c which contains a codeword of weight d for codes of dimension 4 or more upper and lower bounds on the maximum of g sub 2 d sub 2 are given

bootstrap confidence intervals and bootstrap approximations abstract the bca bootstrap procedure efron 1987 for constructing parametric and nonparametric confidence intervals is considered like the bootstrap this procedure can be applied to complicated problems in a wide range of situations for models indexed by a scalar parameter with efficient estimator the bca procedure relies on the existence of a transformation g such that is approximately normally distributed with standard deviation 1 ag although explicit knowledge of g is not required in this article we show how to construct this transformation by generalizing the one given by efron 1987 sec 10 for translation families this construction consists of the composition of a variance stabilizing transformation and a skewness reducing transformation it produces a new interval the bca interval that is asymptotically equivalent to the bca interval and can be computed without bootstrap sampling we also derive from this construction an accurate approximation to the bootstrap distribu

multivariate cusum quality control procedures it is a common practice to use simultaneously several one sided or two sided cusum procedures of the type proposed by page 1954 in this article this method of control is considered to be a single multivariate cusum mcusum procedure methods are given for approximating parameters of the distribution of the minimum of the run lengths of the univariate cusum charts using a new method of comparing multivariate control charts it is shown that an mcusum procedure is often preferable to hotelling s tz procedure for the case in which the quality characteristics are bivariate normal random variables

a note on multivariate cusum procedures cumulative sum cusum procedures are among the most powerful tools for detecting a shift from a good quality distribution to a bad quality distribution this article discusses the natural application of cusum procedures to the multivariate normal distribution it discusses two cases detecting a shift in the mean vector and detecting a shift in the covariance matrix as an example the procedure is applied to measurements taken on optical fibers

extreme value theory for moving average processes this paper studies extreme values in infinite moving average processes x t sum lambda c lambda t z lambda defined from an i i d noise sequence z lambda in particular this includes the arma processes often used in time series analysis a fairly complete extremal theory is developed for the cases when the d f of the z lambda s has a smooth tail which decreases approximately as exp z p as z rightarrow infty for 0 p infty or as a power of z the influence of the averaging on extreme values depends on p and the c lambda s in a rather intricate way for p 2 which includes normal sequences the correlation function r t sum lambda c lambda t c lambda sum lambda c 2 lambda determines extremal behavior while perhaps more surprisingly for p neq 2 correlations have little bearing on extremes further the sample paths of x t near extreme values asymptotically assume a specific nonrandom form which again depends on p and c lambda in an interesting way one use of this latter result is as an informal quantitative check of a fitted moving average or arma model by comparing the sample path behavior predicted by the model with the observed sample paths

generalized hamming weights of linear codes the generalized hamming weight d sub r c of a binary linear code c is the size of the smallest support of any r dimensional subcode of c the parameter d sub r c determines the code s performance on the wire tap channel of type ii bounds on d sub r c and in some cases exact expressions are derived in particular a generalized griesmer bound for d sub r c is presented and examples are given of codes meeting this bound with equality

a statistical control chart for stationary process data in the statistical process control environment a primary method to deal with autocorrelated data is the use of a residual chart although this methodology has the advantage that it can be applied to any autocorrelated data it needs some modeling effort in practice in addition the detection capability of the residual chart is not always great this article proposes a statistical control chart for stationary process data it is simple to implement and no modeling effort is required comparisons are made among the proposed chart the residual chart and other charts when the process autocorrelation is not very strong and the mean changes are not large the new chart performs better than the residual chart and the other charts

performance of cusum control schemes for serially correlated observations this article discusses situations in which one is interested in evaluating the run length characteristics of a cumulative sum control scheme when the underlying data show the presence of serial correlation in practical applications situations of this type are common in problems associated with monitoring such characteristics of data as forecasting errors measures of model adequacy and variance components the discussed problem is also relevant in situations in which data transformations are used to reduce the magnitude of serial correlation the basic idea of analysis involves replacing the sequence of serially correlated observations by a sequence of independent and identically distributed observations for which the runlength characteristics of interest are roughly the same applications of the proposed method for several classes of processes arising in the area of statistical process control are discussed in detail and it is shown that it leads to approximations that can be considered acceptable in

monitoring processes that wander using integrated moving average models often the least appropriate assumption in traditional control charting technology is that process data constitute a random sample in reality most process data are correlated either temporally spatially or due to nested sources of variation one approach to monitoring temporally correlated data uses a control chart on the forecast errors from a time series model of the process with possibly a transfer function term to model compensatory adjustments if the time series term is an integrated moving average then a sudden level shift in the process results in a patterned shift in the mean of forecast errors initially the mean shifts by the same amount as the process level but then it decays geometrically back to 0 corresponding to the ability of the forecast to recover from the upset we study four monitoring schemes umulative sums cusum s exponentially weighted moving averages shewhart individuals charts and a likelihood ratio scheme comparisons of signaling probabilities and average run lengt

on the markov chain approach to the two sided cusum procedure a markov chain representation is given for the two sided possibly asymmetric cumulative sum cusum procedure of ewan and kemp 1960 for integer valued random variables the approach taken by brook and evans 1972 for the one sided cusum procedure to determine exact and approximate expressions for the run length distribution its moments and its percentage points is extended to the two sided cusum procedure the number of states included in the markov chain is minimized in order to make the methods as efftcient as possible

monitoring general linear profiles using multivariate exponentially weighted moving average schemes we propose a statistical process control scheme that can be implemented in industrial practice in which the quality of a process can be characterized by a general linear profile we start by reviewing the general linear profile model and the existing monitoring methods based on this we propose a novel multivariate exponentially weighted moving average monitoring scheme for such a profile we introduce two other enhancement features the variable sampling interval and the parametric diagnostic approach to further improve the performance of the proposed scheme throughout the article we use a deep reactive ion etching example from semiconductor manufacturing which has a profile that fits a quadratic polynomial regression model well to illustrate the implementation of the proposed approach

better bootstrap confidence intervals abstract we consider the problem of setting approximate confidence intervals for a single parameter in a multiparameter family the standard approximate intervals based on maximum likelihood theory can be quite misleading in practice tricks based on transformations bias corrections and so forth are often used to improve their accuracy the bootstrap confidence intervals discussed in this article automatically incorporate such tricks without requiring the statistician to think them through for each new application at the price of a considerable increase in computational effort the new intervals incorporate an improvement over previously suggested methods which results in second order correctness in a wide variety of problems in addition to parametric families bootstrap intervals are also developed for nonparametric situations

bounds for the distribution of the run length of one sided and two sided cusum quality control schemes upper and lower bounds are derived for the distribution of the run length n of both the one sided and two sided cusum schemes based upon a sequence of iterates pr n 0 pr n 1 bounds are constructed such that m n i pr n n pr n n i m n i pr n n holds for all n i 1 2 with constants 0 m n m n 1 suitably chosen the bounds converge monotonically in the sense that m n i 1pr n n m n 1 i pr n n 1 m n i 1pr n n m n 1 i pr n n 1 and under some mild and natural conditions as a by product bounds are presented for the percentage points of the distribution function of n for the average run length of the inspection scheme for the standard deviation of n and finally for the probability mass function of n some numerical results are displayed to demonstrate the effkiency of the method

weighted cumulative sum technique a class of weighted control schemes that generalizes the basic cumulative sum cusum technique is introduced the schemes of the first type in which the weights represent information concomitant with the data prove to be especially useful when handling charts corresponding to samples of varying sizes the schemes of the second type are based on giving greater weight to more recent information representatives of this class are shown to have better run length characteristics with respect to drift in the level of a controlled process than does the classical cusum while maintaining good sensitivity with respect to shifts analogous to the classical cusum scheme they admit a dual graphical representation that is the scheme can be applied by means of a one or two sided decision interval or via a v mask a special case of this type of scheme designated the geometric cusum is considered in detail it can be viewed as a cusum type counterpart of the exponentially weighted moving average

should exponentially weighted moving average and cumulative sum charts be used with shewhart limits traditional shewhart control charts are usually considered effective for detecting large shifts in process parameters but ineffective for detecting small shifts for detecting small parameter shifts it is much better to use exponentially weighted moving average ewma control charts or cumulative sum cusum control charts but these charts are not considered as effective as shewhart charts for large parameter shifts it is frequently recommended that ewma or cusum charts be used in combination with a shewhart chart to gain the benefits of both types of charts so that both small and large shifts can be detected quickly here we consider the problem of process monitoring when a continuous process variable is being observed and the objective is to detect small or large shifts in either the process mean or the process standard deviation in this situation it is customary to use a combination of two control charts one chart designed to monitor and the other designed to monitor for this situation

design of double cusum quality control schemes abstract the simultaneous use of two standard cusum schemes is shown to result in a quality control scheme with a high performance in detecting an arbitrary out of control state the parameters of the so called double cusum scheme are found by adapting the average run length to the ones of a set of locally optimal standard cusum schemes in a best way first the run length distribution is evaluated with the help of an efficient extrapolation method second an optimization problem is stated and solved use is made of some monotonicity results considerably reducing the computational effort

joint statistical design of double sampling x and s charts abstract in statistical quality control usually the mean and variance of a manufacturing process are monitored jointly by two statistical control charts e g a x chart and a r chart because of the efficiency of double sampling ds x charts in detecting shifts in process mean and ds s charts in process standard deviation it seems reasonable to investigate the joint ds x and s charts for statistical quality control in this paper a joint ds x and s chart scheme is proposed the statistical design of the joint ds x and s charts is defined and formulated as an optimization problem and solved using a genetic algorithm the performance of the joint ds x and s charts is also investigated the results of the investigation indicate that the joint ds x and s charts offer a better statistical efficiency in terms of average run length arl than combined ewma and cusum schemes omnibus ewma scheme over certain shift ranges when all schemes are optimized to detect certain shifts in comparison with the joint standard two stage samplings and variable sampling size x and r charts the joint ds charts offer a better statistical efficiency for all ranges of the shifts

using an autoregressive model to detect departures from steady states in unequally spaced tumour biomarker data a new method based on a continuous time autoregressive car 1 model of time series data is provided for detecting departures of tumour markers from steady states in breast cancer patients following surgery a kalman filter recursive algorithm is used to calculate the likelihood function arising from the car 1 model and to calculate recursive residuals which are monitored by a shewhart cusum scheme this approach can be used to monitor the serial marker data of large numbers of patients even when the series are short and the data are serially correlated and unequally spaced further the methodology can be used to recommend appropriate testing intervals

a rank based multivariate cusum procedure we consider statistical process control when measurements are multivariate a cumulative sum cusum procedure is suggested in detecting a shift in the mean vector of the measurements which is based on the cross sectional antiranks of the measurements at each time point the measurements are ordered and their antiranks which are the indices of the order statistics are recorded when the process is in control and the joint distribution of the multivariate measurements satisfies some regularity conditions the antirank vector at each time point has a given distribution this distribution changes to some other distribution when the process is out of control and the components of the shift in the mean vector of the process are not all the same this cusum can therefore detect shifts in all directions except the one in which the components of the shift in the mean vector are all the same but not 0 the shift with equal components however can be easily detected by another univariate cusum the former cusu

evaluation of the run length probability distribution for cusum charts assessing chart performance this article provides a fast and accurate algorithm to compute the run length probability distribution for cumulative sum charts to control process mean this algorithm uses a fast and numerically stable recursive formula based on accurate gaussian quadrature rules throughout the whole range of the computed run length distribution and therefore improves the numerical efficiency and accuracy of existing methods the algorithm may detect whether or not the geometric approximation is adequate and when it is possible it allows switching to the geometric recursion the procedure may be applied not only to the normal distribution but also to nonsymmetric and long tailed continuous distributions some examples of which are provided methods to assess chart performance according to the run length distribution as well as some multivariate issues in statistical process control are considered

joint economic design of ewma control charts for mean and variance abstract control charts with exponentially weighted moving average ewma statistics mean and variance are used to jointly monitor the mean and variance of a process an ewma cost minimization model is presented to design the joint control scheme based on pure economic or both economic and statistical performance criteria the pure economic model is extended to the economic statistical design by adding constraints associated with in control and out of control average run lengths the quality related production costs are calculated using taguchi s quadratic loss function the optimal values of smoothing constants sampling interval sample size and control chart limits are determined by using a numerical search method the average run length of the control scheme is computed by using the markov chain approach computational study indicates that optimal sample sizes decrease as the magnitudes of shifts in mean and or variance increase and higher values of quality loss coefficient lead to shorter sampling intervals the sensitivity analysis results regarding the effects of various inputs on the chart parameters provide useful guidelines for designing an ewma based process control scheme when there exists an assignable cause generating concurrent changes in process mean and variance

on the construction of band matrices from spectral data abstract we consider 2m 1 banded hermitian skew hermitian or complex symmetric matrices a we derive a set of eigenvalues of a and certain of its submatrices whose knowledge enables us to reconstruct a uniquely the dependence of a on the spectral data is shown to be continous

an algorithm for detecting a change in a stochastic process the problem of detecting a change from one given stationary and ergodic stochastic process to another such process is considered it is assumed that both stochastic processes are processes with memory and that they are mutually independent a sequential test is proposed and analyzed it is proved that the proposed test is asymptotically optimal in a mathematically precise sense

approximate tail probabilities for the maxima of some random fields abstract hogan and siegmund 1986 adapt the method developed by pickands 1969 qualls and watanabe 1973 and bickel and rosenblatt 1973 to obtain explicit large deviation approximations for the maxima of several gaussian random fields arising in statistics using a special argument for one particular case they suggest a heuristic second order approximation for that case and they show by a monte carlo experiment that the second order approximation frequently gives considerably better numerical results the purpose of this paper is to show that the method developed by woodroofe 1976 1982 for problems in one dimensional time can be adapted to study maxima of random fields overall it involves simpler computations than the previous method and consequently seems potentially capable of delivering a genuine second order approximation should one seem desirable

a new two sided cumulative sum quality control scheme a new two sided cumulative sum quality control scheme is proposed the new scheme was developed specifically to be generalized to a multivariate cumulative sum quality control scheme the multivariate version will be examined in a subsequent paper this article evaluates the univariate version a comparison of the conventional two sided cumulative sum scheme and the proposed scheme indicates that the new scheme has slightly better properties ratio of on aim to off aim average run lengths than the conventional scheme steady state average run lengths are discussed the new scheme and the conventional two sided cumulative sum scheme have equivalent steady state average run lengths methods for implementing the fast initial response feature for the new cumulative sum scheme are given a comparison of average run lengths for the conventional and proposed schemes with fast initial response features is also favorable to the new scheme a markov chain approximation is used to calculate the average run lengths

external theory for stochastic processes abstract the purpose of this paper is to provide an overview of the asymptotic distributional theory of extreme values for a wide class of dependent stochastic sequences and continuous parameter processes the theory contains the standard classical extreme value results for maxima and extreme order statistics as special cases but is richer on account of the diverse behavior possible under dependence in both discrete and continuous time contexts emphasis is placed on stationary cases but other important classes e g mark of sequences are included significant ideas and methods are described rather than details and in particular the nature and role of important underlying point processes such as exceedances and upcrossings are emphasized applications are given to particular classes of process e g normal moving average and connections with related theory such as convergence of sums are indicated

generalized hamming weights for linear codes motivated by cryptographical applications the algebraic structure of linear codes from a new perspective is studied by viewing the minimum hamming weight as a certain minimum property of one dimensional subcodes a generalized notion of higher dimensional hamming weights is obtained these weights characterize the code performance on the wire tap channel of type ii basic properties of generalized weights are derived the values of these weights for well known classes of codes are determined and lower bounds on code parameters are obtained several open problems are also listed

the distribution of the run length of one sided cusum procedures for continuous random variables a method is given for approximating the moments and percentage points of the run length distribution of one sided cusum procedures for continuous random variables the run length probabilities are calculated recursively using numerical quadrature until the ratio of successive probabilities stabilizes this ratio and the probabilities of low run length values are then used to approximate parameters of the run length distribution the accuracy of this method is compared with that of previous methods in examples involving normally distributed random variables

cusum charts with variable sampling intervals a standard cumulative sum cusum chart for controlling the process mean takes samples from the process at fixed length sampling intervals and uses a control statistic based on a cumulative sum of differences between the sample means and the target value this article proposes a modification of the standard cusum scheme that varies the time intervals between samples depending on the value of the cusum control statistic the variable sampling interval vsi cusum chart uses short sampling intervals if there is an indication that the process mean may have shifted and long sampling intervals if there is no indication of a change in the mean if the cusum statistic actually enters the signal region then the vsi cusum chart signals in the same manner as the standard cusum chart a markov chain approach is used to evaluate properties such as the average time to signal and the average number of samples to signal results show that the proposed vsi cusum chart is considerably more efficient than the standard cus

the non central chi square chart with two stage samplings abstract in this paper we consider the non central chi square chart with two stage samplings during the first stage one item of the sample is inspected and depending on the result the sampling is either interrupted or it goes on to the second stage where the remaining sample items are inspected and the non central chi square statistic is computed the proposed chart is not only more sensitive than the joint x and r charts but operationally simpler too particularly when appropriate devices such as go no go gauges can be used to decide if the sampling should go on to the second stage or not

exponentially weighted moving average control schemes properties and enhancements roberts 1959 first introduced the exponentially weighted moving average ewma control scheme using simulation to evaluate its properties he showed that the ewma is useful for detecting small shifts in the mean of a process the recognition that an ewma control scheme can be represented as a markov chain allows its properties to be evaluated more easily and completely than has previously been done in this article we evaluate the properties of an ewma control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value a design procedure for ewma control schemes is given parameter values not commonly used in the literature are shown to be useful for detecting small shifts in a process in addition several enhancements to ewma control schemes are considered these include a fast initial response feature that makes the ewma control scheme more sensitive to start up problems a combined shewhart ewma that provides protection against both larg

control charts and the efficient allocation of sampling resources control charts for monitoring the process mean and process standard deviation are often based on samples of n 1 observations but in many applications individual observations are used n 1 in this article we investigate the question of whether it is better from the perspective of statistical performance to use n 1 or n 1 we assume that the sampling rate in terms of the number of observations per unit time is fixed so using n 1 means that samples can be taken more frequently than when n 1 the best choice for n depends on the type of control chart being used so we consider shewhart exponentially weighted moving average ewma and cumulative sum cusum charts for each type of control chart we investigate a combination of two charts one chart designed to monitor and the other designed to monitor most control chart comparisons in the literature assume that a special cause produces a sustained shift in a process parameter that lasts until the shift is detected we also consider

analysis of cusum and other markov type control schemes by using empirical distributions the run length distribution of a cumulative sum control scheme when the underlying distribution of the incoming observations is unknown is discussed given a sample of size n from this distribution the estimators related to various characteristics of the run length can be obtained by using the empirical cdf instead of the true cdf in the standard analysis procedure the article discusses the properties of the resulting point estimators as well as interval estimators obtained by using resampling techniques applications of the technique for the purpose of design and analysis of control schemes are also discussed the proposed methodology can be easily adapted for other markov type control schemes

on the second relative greedy weight of 4 dimensional codes based on applications to the wire tap channel of type ii with the coset coding scheme we will define optimal codes which are described as the ones achieving upper bounds on e 2 m 2 where e 2 and m 2 are the second relative greedy weight and the second relative generalized hamming weight respectively by using the finite geometry method we will first classify all the 4 dimensional codes with respect to their k 1 dimensional subcodes for k 1 1 and k 1 2 and determine the upper bounds on e 2 m 2 for each class of codes and then obtain optimal codes by explicitly constructing the value assignments which achieve upper bounds on e 2 m 2

the weight hierarchies of q ary codes of dimension 4 the weight hierarchy of a linear n k q code c over gf q is the sequence d sub 1 d sub 2 d sub k where d sub spl tau is the smallest support of an spl tau dimensional subcode of c the possible weight hierarchies of n 4 q codes are studied in particular the possible weight hierarchies of n 4 3 codes are determined

inverse eigenvalue problem in structural design a method is presented for the design of a structure with specified low order natural frequencies the method can also be used for design problems in which certain bands devoid of any frequency constraints are specified this can further be used to generate initial feasible designs for optimum design problems with frequency constraints

inverse eigenvalue problems a collection of inverse eigenvalue problems are identified and classified according to their characteristics current developments in both the theoretic and the algorithmic aspects are summarized and reviewed in this paper this exposition also reveals many open questions that deserve further study an extensive bibliography of pertinent literature is attached

using autoregressive and random walk models to detect trends and shifts in unequally spaced tumour biomarker data continuous time autoregressive car 1 and random walk models of time series data are provided for detecting non random shifts and trends of tumour markers in breast cancer patients following resection for cure the continuous time random walk model with observation error is extended to the case of multiple patient time series these models can be used to monitor large numbers of patients with time series with few sampling events that are serially correlated and unequally spaced further the methodologies can be used to recommend appropriate testing intervals a kalman filter recursive algorithm is used to calculate the likelihood functions arising from the car 1 and random walk models and to calculate recursive residuals which are monitored by shewhart cusum schemes

inverse spectral analysis for a class of infinite band symmetric matrices abstract this work deals with the direct and inverse spectral analysis for a class of infinite band symmetric matrices this class corresponds to operators arising from difference equations with usual and inner boundary conditions we give a characterization of the spectral functions for the operators and provide necessary and sufficient conditions for a matrix valued function to be a spectral function of the operators additionally we give an algorithm for recovering the matrix from the spectral function the approach to the inverse problem is based on the rational interpolation theory

a simple method for studying run length distribution of exponentially weighted moving average charts a numerical procedure using integral equations is presented for the tabulation of moments of run lengths of exponentially weighted moving average ewma charts both average run lengths arl s and standard deviations of run lengths sdrl s are presented for the twosided ewma chart assuming normal observations along with an example illustrating how to design such a chart the procedure given extends easily to many nonnormal cases and to one sided versions of the ewma chart

comparisons of some exponentially weighted moving average control charts for monitoring the process mean and variance an exponentially weighted moving average ewma control chart for monitoring the process mean may be slow to detect large shifts in when the ewma tuning parameter is small an additional problem sometimes called the inertia problem is that the ewma statistic may be in a disadvantageous position on the wrong side of the target when a shift in occurs which may significantly delay detection of a shift in options for improving the performance of the ewma chart include using the ewma chart in combination with a shewhart chart or in combination with an ewma chart based on squared deviations from target the ewma chart based on squared deviations from target is designed to detect increases in the process standard deviation but it is also very effective for detecting large shifts in capizzi and masarotto recently proposed the option of an adaptive ewma control chart in which is a function of the data with the adaptive feature the ewma chart behaves like a standard ewma chart when the curren