Cumulative Sum Test Research Articles

Asymptotic Performance Analysis of Distributed Non-Bayesian Quickest Change Detection With Energy Harvesting Sensors

This article focuses on the distributed non-Bayesian quickest change detection based on the cumulative sum (CUSUM) algorithm in an energy harvesting wireless sensor network, where the distributions before and after the change point are assumed to be known. Each sensor is powered by randomly available harvested energy from the surroundings. It samples the observation signal and computes the log-likelihood ratios (LLRs) of the aforementioned two distributions if enough energy is available in its battery for sensing and processing the sample ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$E_{s}$</tex-math></inline-formula> ). Otherwise, the sensor decides to abstain from the sensing process during that time slot and waits until it accumulates enough energy to perform the sensing and processing of a sample. This LLR is used for performing the CUSUM test to arrive at local decisions about the change point, which are then combined at the fusion center (FC) by a predecided fusion rule to arrive at a global decision. In this article, we derive the asymptotic expressions (as the average time to a false alarm goes to infinity) for the expected detection delay and the expected time to a false alarm at the FC for three common fusion rules, namely, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</small> , <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</small> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> out of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> majority rule, respectively, by considering the scenario, where the average harvested energy at each sensor is greater than the energy required for sensing and processing a sample <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$E_{s}$</tex-math></inline-formula> . To this end, we use the theory of order statistics and the asymptotic distribution of the first passage times of the local decisions. Numerical results are also provided to support the theoretical claims.

Time Series Analysis and structural break detection: A case of Zambia’s CPI.

Purpose: By empirically examining Zambia’s CPI between 2010 and 2020, the study attempts to determine the structural change in the time series. The CPI is one of the most important variables for analyzing inflation in macroeconomics, therefore any change in the dynamic must be determined. In this paper change points and dates are highlighted and statistical analysis methods have been employed to explore and discover the underlying patterns and trends of Zambia’s CPI for the past 10 years.&#x0D; Methodology/approach: Secondary Data from Zambia Statistics Agency (ZamStats.gov.zm) was used for the Study. From 132 elements of observations of time series for 10 years, the detection methods of structural change were employed. The Cumulative Sum Tests (CUSUM test) of Ordinary Least Squares (OLS), Andrew Sup F test, Bai and Perron test, and Chow test were used to detect the model stability and verify the hypothesis using P-value.&#x0D; Results: The results show that there were five (5) Structural changes or breaks in mean and variance and these were February 2012, February 2014, October 2015, October 2017, and May 2019. The structural breaks are highly suggestive as they appear to broadly coincide with readily identifiable macroeconomic events, increased stock of external debt following the issuance of Eurobonds in 2012, 2014, and 2015, rise increased food prices arising from the adverse impact of erratic rainfall on agricultural output and the pass-through from the depreciation of the Kwacha.&#x0D; Policy Implication: Based on the study, strong and sound macroeconomic policies are needed to be implemented: Such as debt management and diversification of foreign exchange sources, and increased earnings.

Time Series Analysis on the Effect of Organic and Inorganic Fertilizer in Production of Teff (Eragrostis tef) in Ethiopia

Ethiopia is the only country that produces teff (Eragrostis tef) in the world, but the production of teff is still low compared to other cereal crops. The main purpose of this study was to evaluate the impacts of organic and inorganic fertilizers on teff yield between 1995 and 2017 using an autoregressive distributed lag with co-integration approach. The Phillips Peron test and the Augmented Dickey-Fuller test for time series stationary checking and capturing serial correlation were used, whereas the Breusch Godfrey Lagrange multiplier and the cumulative sum test were used for determining the autocorrelation of residuals and the stability of coefficients. The result showed that the average yield of teff was 11.25 quintals per hectare, and the bounded F-test for co-integration among the variables does not show evidence of a long-run relationship between fertilizers applied and teff yield per hectare. The quantity of urea applied had a positive and significant impact on the yield of teff, and the quantity of di-ammonium phosphate (DAP) application had a negative impact in the short run. In conclusion, there was no co-integration between the yield of teff crops and fertilizer types such as urea and di-ammonium phosphate, which indicates that the quantity of urea fertilizer used for the production of teff has a positive relationship while the quantity of DAP fertilizer had an indirect relationship. Hence inorganic fertilizer like DAP is negatively correlated with the production of teff while organic fertilizer (urea) is positively correlated.

Incipient Residual-Based Anomaly Detection in Power Electronic Devices

Power electronics (PE) and high-frequency switching circuits are key to superior performance of electric vehicles. It is vital to monitor the condition of the PE components in real-time for safety and reliability. In this article, we propose two anomaly detection methods based on a combination of data preprocessing to suppress noise and outliers, multivariate regression models to predict signals of interest under nominal operation, and sequential analysis of residuals. In particular, the methods utilize median filtering to extract <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -state medians in each switching cycle in nonlinear autoregressive exogenous neural network models or filtered <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -state data in partial least squares-based models to represent the nominal circuit behavior. Optimal and approximate dynamic programming-based feature selection methods are developed to select the most informative signals or their transformations. Predictions from the learned models are used to generate the residuals for anomaly detection by Page’s cumulative sum test. The proposed models and anomaly detection methods are validated on three accelerated aging experimental datasets, comprised of 60 power <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> devices with low-frequency and high-frequency switching under disparate operating conditions. Due to the simplicity and efficiency of the data-driven anomaly detection schemes, the proposed methods can potentially be embedded in real-time digital platforms.

BACKWARD CUSUM FOR TESTING AND MONITORING STRUCTURAL CHANGE WITH AN APPLICATION TO COVID-19 PANDEMIC DATA

It is well known that the conventional cumulative sum (CUSUM) test suffers from low power and large detection delay. In order to improve the power of the test, we propose two alternative statistics. The backward CUSUM detector considers the recursive residuals in reverse chronological order, whereas the stacked backward CUSUM detector sequentially cumulates a triangular array of backwardly cumulated residuals. A multivariate invariance principle for partial sums of recursive residuals is given, and the limiting distributions of the test statistics are derived under local alternatives. In the retrospective context, the local power of the tests is shown to be substantially higher than that of the conventional CUSUM test if a break occurs in the middle or at the end of the sample. When applied to monitoring schemes, the detection delay of the stacked backward CUSUM is found to be much shorter than that of the conventional monitoring CUSUM procedure. Furthermore, we propose an estimator of the break date based on the backward CUSUM detector and show that in monitoring exercises this estimator tends to outperform the usual maximum likelihood estimator. Finally, an application of the methodology to COVID-19 data is presented.

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Non-Stationary Trend Change Point Pattern Using 24-Hourly Annual Maximum Series (AMS) Precipitation Data

This paper mainly investigated the basic information about non-stationary trend change point patterns. After performing the investigation, the corresponding results show the existence of a trend, its magnitude, and change points in 24-hourly annual maximum series (AMS) extracted from monthly maximum series (MMS) data for thirty years (1986-2015) rainfall data for Uyo metropolis. Trend analysis was performed using Mann-Kendall (MK) test and Sen’s slope estimator (SSE) used to obtain the trend magnitude, while the trend change point analysis was conducted using the distribution-free cumulative sum test (CUSUM) and the sequential Mann-Kendall test (SQMK). A free CUSUM plot date of change point of rainfall trend as 2002 at 90% confidence interval was obtained from where the increasing trend started and became more pronounced in the year 2011, another change point year from the SQMK plot with the trend intensifying. The SSE gave an average rate of change in rainfall as 2.1288 and 2.16 mm/year for AMS and MMS time series data respectively. Invariably, the condition for Non-stationary concept application is met for intensity-duration-frequency modeling.

Learning curves of robot-assisted pedicle screw fixations based on the cumulative sum test.

BACKGROUNDIn robot-assisted (RA) spine surgery, the relationship between the surgical outcome and the learning curve remains to be evaluated.AIMTo analyze the learning curve of RA pedicle screw fixation (PSF) through fitting the operation time curve based on the cumulative summation method.METHODSRA PSFs that were initially completed by two surgeons at the Beijing Jishuitan Hospital from July 2016 to March 2019 were analyzed retrospectively. Based on the cumulative sum of the operation time, the learning curves of the two surgeons were drawn and fit to polynomial curves. The learning curve was divided into the early and late stages according to the shape of the fitted curve. The operation time and screw accuracy were compared between the stages.RESULTSThe turning point of the learning curves from Surgeons A and B appeared in the 18th and 17th cases, respectively. The operation time [150 (128, 188) min vs 120 (105, 150) min, P = 0.002] and the screw accuracy (87.50% vs 96.30%, P = 0.026) of RA surgeries performed by Surgeon A were significantly improved after he completed 18 cases. In the case of Surgeon B, the operation time (177.35 ± 28.18 min vs 150.00 ± 34.64 min, P = 0.024) was significantly reduced, and the screw accuracy (91.18% vs 96.15%, P = 0.475) was slightly improved after the surgeon completed 17 RA surgeries.CONCLUSIONAfter completing 17 to 18 cases of RA PSFs, surgeons can pass the learning phase of RA technology. The operation time is reduced afterward, and the screw accuracy shows a trend of improvement.

On Covert Communication Against Sequential Change-Point Detection

We investigate covert communication under a sequential change-point detection (SCPD) framework, where a transmitter, Alice, attempts to communicate reliably with a receiver, Bob, over an additive white Gaussian noise channel, while simultaneously ensuring covertness (low probability of detection) with respect to an adversary, Willie. Different from the binary hypothesis test based detection framework considered in prior works where Willie collects all signal samples together and makes a decision in a batch manner, we view Willie’s detection process as an SCPD process that works in a real-time manner. We establish a new criterion to evaluate the covertness of the communication between Alice and Bob, and investigate the performance of covert communication accordingly. Subject to the proposed constraint on covertness, we investigate the feasible transmit power and transmission duration under three SCPD algorithms, namely, the Shewhart test, the finite moving average chart (FMAC), and the cumulative sum (CUSUM) test, and characterize how the covert communication throughput scales with the average run length to false alarm (ARL2FA) of Willie’s detector as the ARL2FA increases without bound. Our theoretical results can be viewed as upper bounds on the covert communication throughput that can be achieved, and we show that compared with the case where Willie performs the CUSUM test, Alice and Bob achieve a higher covert communication throughput if Willie performs the Shewhart test or the FMAC.

Randomness of Möbius coefficients and Brownian motion: growth of the Mertens function and the Riemann hypothesis

The validity of the Riemann hypothesis (RH) on the location of the non-trivial zeros of the Riemann ζ-function is directly related to the growth of the Mertens function , where μ(k) is the Möbius coefficient of the integer k; the RH is indeed true if the Mertens function goes asymptotically as M(x) ∼ x 1/2+ϵ , where ϵ is an arbitrary strictly positive quantity. We argue that this behavior can be established on the basis of a new probabilistic approach based on the global properties of the Mertens function, namely, based on reorganizing globally in distinct blocks the terms of its series. With this aim, we focus attention on the square-free numbers and we derive a series of probabilistic results concerning the prime number distribution along the series of square-free numbers, the average number of prime divisors, the Erdős–Kac theorem for square-free numbers, etc. These results point to the conclusion that the Mertens function is subject to a normal distribution as much as any other random walk. We also present an argument in favor of the thesis that the validity of the RH also implies the validity of the generalized RH for the Dirichlet L-functions. Next we study the local properties of the Mertens function, i.e. its variation induced by each Möbius coefficient restricted to the square-free numbers. Motivated by the natural curiosity to see how closely to a purely random walk any sub-sequence is extracted by the sequence of the Möbius coefficients for the square-free numbers, we perform a massive statistical analysis on these coefficients, applying to them a series of randomness tests of increasing precision and complexity; together with several frequency tests within a block, the list of our tests includes those for the longest run of ones in a block, the binary matrix rank test, the discrete Fourier transform test, the non-overlapping template matching test, the entropy test, the cumulative sum test, the random excursion tests, etc, for a total of 18 different tests. The successful outputs of all these tests (each of them with a level of confidence of 99% that all the sub-sequences analyzed are indeed random) can be seen as impressive ‘experimental’ confirmations of the Brownian nature of the restricted Möbius coefficients and the probabilistic normal law distribution of the Mertens function analytically established earlier. In view of the theoretical probabilistic argument and the large battery of statistical tests, we can conclude that while a violation of the RH is strictly speaking not impossible, it is however extremely improbable.

Person-dependent seizure detection using statistical CUSUM detector: Preliminary results

Epilepsy is a chronic brain disorder, and for at least one- third of epilepsy patients, medications do not adequately control seizures and surgery is the only potential cure. An automated seizure detector that requires a short period of normal EEG would shorten the seizure monitoring durations, decrease the need for manual assessment of large amount of recorded EEG data, and accordingly assist the neurologists focus on improving quality of care. In this work, we propose a novel approach based on the extended cumulative sum test to detect seizures using intracranial EEG. Different than the existing machine learning approaches, this method requires only a short period of normal EEG for training. In addition to a new proposed feature based on partial directed coherence and random graph theory, in this work, previously developed features used to characterize seizures are used as features in cumulative sum test for seizure detection. A total of 33 intracranial EEG recordings collected from a total of 9 patients corresponding to three different datasets have been used for the analysis purposes: (i) the first dataset includes 11 recorded EEG files (~12.5 min) from 4 patients; (ii) the second dataset includes 20 recorded EEG files (~30 min) from 4 patients; and (iii) two 24-hour long EEG files from a single patient. The proposed detector achieved a mean sensitivity and mean specificity for the first (0.77, 0.86), second (0.88, 0.9) and third datasets (0.92, 0.94) respectively. Statistical detection of seizures has shown success in detecting seizures without the need for highly customizable parameters or previously labelled EEG data. The proposed method could be used in real-time at hospital settings with a minimum requirement of training data.

A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines

This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.

Change Point Test for the Conditional Mean of Time Series of Counts Based on Support Vector Regression.

This study considers support vector regression (SVR) and twin SVR (TSVR) for the time series of counts, wherein the hyper parameters are tuned using the particle swarm optimization (PSO) method. For prediction, we employ the framework of integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) models. As an application, we consider change point problems, using the cumulative sum (CUSUM) test based on the residuals obtained from the PSO-SVR and PSO-TSVR methods. We conduct Monte Carlo simulation experiments to illustrate the methods’ validity with various linear and nonlinear INGARCH models. Subsequently, a real data analysis, with the return times of extreme events constructed based on the daily log-returns of Goldman Sachs stock prices, is conducted to exhibit its scope of application.

A new limit result in change point analysis

Change points are abrupt changes in time ordered data. The cumulative sum (CUSUM) test statistic is an important statistic for testing for a change point. In this article, we prove a functional central limit theorem for heterogeneous α-mixing triangular array in assuming a Riemann integrable variance function. We investigate the asymptotic behavior of the CUSUM statistic under the null and alternative hypotheses through a simulation.

Analysis and visualization of meteorological extremes in humid subtropical regions

Understanding the variability in extreme meteorological events due to climate change and anthropogenic activities is crucial to ensure sustainable development. In this paper, the variation in precipitation and temperature in the Upper Narmada Basin (UNB), a humid subtropical region of central India, during 1901–2002, was analyzed using the Mann-Kendall (MK) test with the Sen’s slope (SS) estimator and innovative trend analysis (ITA). As a result, a significant negative trend in the annual precipitation occurred at two stations (12.5%) in the UNB, whereas no well-defined significant seasonal trends were detected. Moreover, in the annual mean temperature, a significant increasing trend occurred in the basin, with the highest and lowest rates of 0.470 °C/10 year and 0.287 °C/10 year, respectively. The seasonal temperature exhibited a significant increasing trend in the spring and autumn seasons. The trends in the extreme values were investigated using the ITA method. Furthermore, a quantitative study was carried out to measure the suitability of the ITA by comparing it with nonparametric methods (the MK test and SS estimator). The results endorse the reliability of the ITA method and show strong agreement between the trends and statistics of the parametric (ITA) and nonparametric approaches. To visualize the extreme events in meteorological series, a discrete wavelet transform (DWT) was employed with the Daubechies (db3) mother wavelet. In addition, change points were detected using the sequential Mann-Kendall (SQMK) and cumulative sum (CUSUM) tests. Significant change points in the annual precipitation were detected at nine stations (56.25%) during 1955–1958, while only three stations (18.75%) exhibited the annual mean temperature during 1960. The findings of this study could provide support to understand the variability in the meteorological trend over the basin and can help with water resource planning in future development.

Outcomes of acute type A aortic dissection operations performed by early-career cardiovascular surgeons

ObjectiveSurgical outcomes of acute type A aortic dissection have been recognized to be associated with the surgical volume of individual hospitals and surgeons. In this study, we aimed to investigate the results and learning curves of acute type A aortic dissection operations performed by early-career cardiovascular surgeons. MethodsA total of 248 surgical repairs of acute type A aortic dissections were conducted at a tertiary medical center between 2010 and 2018. By using the cumulative sum test, cardiovascular surgeons in their early career were identified, and their performances were assessed. The outcomes of patients who were operated by early-career cardiovascular surgeons were compared with those by experienced or senior surgeons. ResultsDuring the study period, 202 (81.5%) of the 248 acute type A aortic dissection operations were performed primarily by the 4 newly appointed attending cardiovascular surgeons. In cumulative sum curves, all surgeons exhibited a steady performance throughout the study period. On the basis of our institutional result of acute type A aortic dissection operation, early career was defined as performing fewer than 32 acute type A aortic dissection operations. The 30-day mortality rates of acute type A aortic dissection operations performed by early-career surgeons were equivalent to those performed by experienced/senior surgeons (10.9% vs 12.5%, P = .844). There was also no difference in mid-term overall survival and aortic event-free survival between the 2 groups (P = .638 and P = .574, respectively). ConclusionsIn a center with a well-established program, cardiovascular surgeons could accomplish surgical repair of acute type A aortic dissection with adequate early- and mid-term results from the initiation of their careers.

Exponential family QMLE-based CUSUM test for integer-valued time series

This study considers testing for parameter changes in integer-valued time series models based on one parameter exponential family quasi-maximum likelihood estimates. We employ the cumulative sum (CUSUM) test as a change point test, based on score vectors, residuals, standardized residuals, and squares of residuals, focusing on performance comparison of negative binomial (NB) quasi-maximum likelihood estimate (QMLE)-based tests. Monte Carlo simulations are conducted with Poisson, NB, zero-inflated Poisson and NB, generalized Poisson INGARCH(1,1) models and Poisson and NB INAR(1) models. Real data analysis using the Korean stock price index (KOSPI) 200 is also included for illustration. The findings validate QMLE-based CUSUM tests using standardized residuals for INGARCH models and residuals for INAR models. Zero proportional change is well detected by the test using squares of residuals.

Fault detection strategy combining NARMAX model and Bhattacharyya distance for process monitoring

The complexity of modern chemical and petrochemical plants is becoming increasingly problematic in the recent years. At the same time, the demands to ensure safety and reliability of process operations rise. Early detection of abnormal event in complex real systems decrease maintenance cost and lead to guarantee the safety of human operators and environment. In the present work, a fault detection (FD) method which exploits the advantages of black-box modeling and statistical measure for fault detection in real chemical process as a distillation column is proposed. This technique is developed by applying the Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) model and Bhattacharyya distance (BD). In order to determine the NARMAX model, a real data set recorded during normal operations is used. Then, the BD is used to quantify on-line the dissimilarity between the current and reference probability distributions of the residual obtained from the NARMAX model for fault detection purposes. The ability of the proposed FD approach is demonstrated using real fault of separation unit. The obtained results indicate that the developed technique produces favorable performance compared to the conventional Cumulative Sum (CUSUM) test.

An examination of human capital development and economic growth in Nigeria

The study investigates the relationship between human capital development proxies and economic growth in Nigeria. The main motivation is to empirically determine if human capital development proxies have the capacity to enhance economic growth. The study used annual data from 1980-2019 and the variables are government expenditure on primary school enroelment, goverment expenditure on secondary school enrolment, government expenditure on tertary school enrolment, government expenditure on health services and real gross domestic product. The study employed Unit Root test for stationarity, Auto regressive distributed lags (ARDL) to Co-integration test and ECM test to determine the speed of adjustment from the short to its long run equilibrium and looked at the trends analysis of the data in the model. Furthermore, pre-diagnostic and post diagnostic test were carried out to estimate the variables. The log-linear Ordinary Least Square result indicates that R2 is 99 per cent and that given the F* value of 1560.036, the entire model is internally consistent. There is no auto-correlation in the model since DW is 1.732938 and close to 2. The results shows that variables are integrated of order 1(0) and 1(1) thereby establishing that the variables are co-integrated. The ECM (t-1) value of -51 per cent shows that it is rightly signed and is able to correct, adjust and tie the short run dynamics with the long run equilibrium with a speed of about six months. The post test results reveal that Heteroskedasticity and serial correlation is not aproblem in the model. The study also discovers in addition some level of structural stability in the model using Cumulative Sum (CUSUM) test. The study concludes that human capital development enhances economic growth in the long run and recommends government wholistic re-focused attention on education sector due to the productive link between human capital development economic growths.

Determination of mononuclear cell count using peripheral smear and flow cytometry in peripheral blood stem cell products: A retrospective study from an Indian cancer center.

BACKGROUND:Mononuclear cells (MNCs) are considered equivalent to hematopoietic stem cells, and differential count using peripheral smear was routinely practiced to enumerate MNC. Flow cytometry plots used for CD34 enumeration assay can also be used in MNC enumeration as it counts more WBC events than manual methods. The aim was to determine the relationship and degree of agreement between peripheral smear and flow cytometry in MNC enumeration of peripheral blood stem cell (PBSC) products.METHODS:In 63 patients, 73 PBSC products were collected between January 2017 and September 2019. The differences in MNC count estimated by peripheral smear method and from flow cytometry plots used for CD34 enumeration were analyzed using Mann–Whitney test. Agreement between the two methods for MNC enumeration was determined by regression analysis. Receiver operating characteristic curve was performed to determine MNC threshold in peripheral blood and PBSC product for adequate mobilization and harvest.RESULTS:There was no difference in enumeration of median MNC count between peripheral smear and flow cytometry (52% vs. 59%, P = 0.185) in PBSC product. However, regression analysis indicated a constant and proportional difference between the methods with r = 0.52. Cumulative sum test for linearity showed deviation from linearity (P = 0.04). MNC counts in peripheral blood failed to achieve discrimination capacity in predicting adequate CD34+ yield/kg body weight in product.CONCLUSION:Peripheral smear estimated lower MNC counts than flow cytometry with weaker agreements between the two methods. Hence, MNC count derived from flow cytometry plot can substitute peripheral smear method for MNC dose calculations. MNC dose at 3.4 × 108/kg consistently predicted >2 × 106/kg CD34+ cells collected.