arXiv subjects

Edward Valachovic

Publications and source records attributed to Edward Valachovic.

At least 19 recordsLinked to original sources

arXiv2026

The Periodically Correlated Components of Measles in New York City using the Variable Band-pass Periodic Block Bootstrap

Measles, a highly contagious, deadly virus, is at risk of losing its eradication status in the Unites States. Understanding the pattern, including seasonality, of measles could provide great benefit for forecasting, prevention, and public health preparedness as the virus re-emerges. The novel Variable Band-pass Periodic Block Bootstrap (VBPBB), which suppresses noise and interfering signals, is more efficient and statistically powerful to find periodic components compared to other existing methods. Using this method, we have found several significant periodic components in historical New York City measles cases which other methods were incapable of identifying. These findings give us a reusable method for understanding measles and other diseases and greater insight into what may occur should measles vaccine rates continue to fall.

stat.AP↗

arXiv2025

Bayesian approach to the PC component

Time series with multiple periodically correlated components is a complex problem with comparatively limited prior research. Most existing time series models are designed to accommodate simple periodically correlated components and tend to be sensitive to over-parameterization and optimization issues and are also unable to model complex PC components patterns in a time series. Frequency separation techniques can be used to maintain the correlation structure of each specific PC component, whereas Bayesian techniques can combine new and existing prior information to update beliefs about these components. This study introduces a method to combine the frequency separation techniques and Bayesian techniques to forecast PC and MPC time series data in a two stage form which is expected to show the new method's suitability in modeling MPC components compared to classical methods.

stat.ME↗

arXiv2025

Assessing Bias in the Variable Bandpass Periodic Block Bootstrap Method

The Variable Bandpass Periodic Block Bootstrap(VBPBB) is an innovative method for time series with periodically correlated(PC) components. This method applies bandpass filters to extract specific PC components from datasets, effectively eliminating unwanted interference such as noise. It then bootstraps the PC components, maintaining their correlation structure while resampling and enabling a clearer analysis of the estimation of the statistical properties of periodic patterns in time series data. While its efficiency has been demonstrated in environmental and epidemiological research, the theoretical properties of VBPBB, particularly regarding its bias of the estimated sampling distributions, remain unexamined. This study investigates issues regarding biases in VBPBB, including overall mean bias and pointwise mean bias, across a range of time series models of varying complexity, all of which exhibit periodic components. Using the R programming language, we simulate various PC time series and apply VBPBB to assess its bias under different conditions. Our findings provide key insights into the validity of VBPBB for periodic time series analysis and offer practical recommendations for its implementation, as well as directions for future theoretical advancements.

stat.ME↗

arXiv2025

Seasonal and periodic patterns of ischemic heart disease in New York using the Variable Multiple Bandpass Periodic Block Bootstrap

Seasonal patterns of the incidence, hospital visits, and mortality of ischemic heart disease (IHD) have been widely reported. This study aims to investigate seasonal and periodic patterns of IHD hospitalizations in New York using a novel bootstrap approach, the Variable Bandpass Periodic Block Bootstrap (VBPBB) method. Using a bandpass filter, VBPBB isolates the periodically correlated (PC) component of interest from other PC components and noise before bootstrapping, preserving correlation structures and yielding more precise 95\% confidence intervals than existing periodic bootstrapping methods. We examine weekly, monthly, and annual patterns, along with their harmonic frequencies, in the IHD hospitalization. In addition to the pre-defined frequencies, we also examine the frequencies with the highest amplitudes in the periodogram. By aggregating bootstrap results from statistically significant PC components, a 95\% CI band that preserves multiple periodic correlation structures was obtained. Statistically significant variation was observed for the weekly, annual component, and its 2nd, 3rd, 5th, and 6th harmonics. CI bands obtained from VBPBB were much narrower than those from existing periodic bootstrapping methods. VBPBB substantially improves the precision of periodic mean estimates while preserving periodic correlation structures, making it suitable for time series with multiple periodic patterns and high noise, such as in environmental or healthcare data.

stat.AP↗

arXiv2025

A Structure-Preserving Assessment of VBPBB for Time Series Imputation Under Periodic Trends, Noise, and Missingness Mechanisms

Incomplete time-series data compromise statistical inference, particularly when the underlying process exhibits periodic structure (e.g., annual or monthly cycles). Conventional imputation procedures rarely account for such temporal dependence, leading to attenuation of seasonal signals and biased estimates. This study proposes and evaluates a structure-preserving multiple imputation framework that augments imputation models with frequency-specific covariates derived via the Variable Bandpass Periodic Block Bootstrap (VBPBB). In controlled simulations, we generate series with annual and monthly components, impose Gaussian noise across low, moderate, and high signal-to-noise regimes, and introduce Missing Completely at Random (MCAR) patterns from 5% to 70% missingness. Dominant periodic components are extracted with VBPBB, resampled to stabilize uncertainty, and incorporated as covariates in Amelia II. Compared with baseline methods that do not model temporal structure, the VBPBB-enhanced approach consistently yields lower imputation error and superior retention of periodic features, with the largest gains observed under high noise and when multiple components are included. These findings demonstrate that explicitly modeling periodic content during imputation improves reconstruction accuracy and preserves time-series structure in the presence of substantial missingness.

stat.AP↗

arXiv2025

Enhancing Data Completeness in Time Series: Imputation Strategies for Missing Data Using Significant Periodically Correlated Components

Missing data is a pervasive issue in statistical analyses, affecting the reliability and validity of research across diverse scientific disciplines. Failure to adequately address missing data can lead to biased estimates and consequently flawed conclusions. In this study, we present a novel imputation method that leverages significant annual components identified through the Variable Bandpass Periodic Block Bootstrap (VBPBB) technique to improve the accuracy and integrity of imputed datasets. Our approach enhances the completeness of datasets by systematically incorporating periodic components into the imputation process, thereby preserving key statistical properties, including mean and variance. We conduct a comparative analysis of various imputation techniques, demonstrating that our VBPBB-enhanced approach consistently outperforms traditional methods in maintaining the statistical structure of the original dataset. The results of our study underscore the robustness and reliability of VBPBB-enhanced imputation, highlighting its potential for broader application in real-world datasets, particularly in fields such as healthcare, where data quality is critical. These findings provide a robust framework for improving the accuracy of imputed datasets, offering substantial implications for advancing research methodologies across scientific and analytical contexts. Our method not only impute missing data but also ensures that the imputed values align with underlying temporal patterns, thereby facilitating more accurate and reliable conclusions.

stat.ME↗

arXiv2025

A Kolmogorov-Zurbenko Fourier Transform Band-pass Filter Extension for Time Series Analysis

This research introduces a novel extension, called the Extended Kolmogorov-Zurbenko Fourier Transform (EKZFT), to an existing class of band-pass filters first introduced by Kolmogorov and Zurbenko. Their original Kolmogorov-Zurbenko Fourier Transform (KZFT) is a useful tool in time series and spatio-temporal analysis, Fourier analysis, and related statistical analysis fields. Example uses of these filters include separating frequencies, filtering portions of the spectra, reconstructing seasonality, investigating periodic signals, and reducing noise. KZFT filters have many practical applications across a wide range of disciplines including health, social, natural, and physical sciences. KZFT filters are band-pass filters defined by three arguments: the length of the filter window; the number of iterations; and the central frequency of the band-pass filter. However, the KZFT filter is limited in design to only positive odd integer widow lengths inherited from the time series. Therefore, for any combination of the other KZFT filter arguments, there is only a relatively small, discrete, selection of possible filter window lengths in a range determined by the size of the dataset. This limits the utility of KZFT filters for many of the stated uses. The proposed EKZFT filter allows a continuous selection of filter window length arguments over the same range, offering improved control, increased functionality, and wider practical use of this band-pass filter. An example application of the EKZFT in a data simulation is provided.

stat.ME↗

arXiv2025

The Variable Multiple Bandpass Periodic Block Bootstrap for Time Series with Multiple Periodic Correlations

This work introduces a novel block bootstrap method for time series with multiple periodically correlated (MPC) components called the Variable Multiple Bandpass Periodic Block Bootstrap (VMBPBB). While past methodological advancements permitted bootstrapping time series to preserve certain correlations, and then periodically correlated (PC) structures, there does not appear to be adequate or efficient methods to bootstrap estimate the sampling distribution of estimators for MPC time series. Current methods that preserve the PC correlation structure resample the original time series, selecting block size to preserve one PC component frequency while simultaneously and unnecessarily resampling all frequencies. This destroys PC components at other frequencies. VMBPBB uses bandpass filters to separate each PC component, creating a set of PC component time series each composed principally of one component. VMBPBB then resamples each PC component time series, not the original MPC time series, with the respective block size preserving the correlation structure of each PC component. Finally, VMBPBB aggregates the PC component bootstraps to form a bootstrap of the MPC time series, successfully preserving all correlations. A simulation study across a wide range of different MPC component frequencies and signal-to-noise ratios is presented and reveals that VMBPBB almost universally outperforms existing methods that fail to bandpass filter the MPC time series.

stat.ME↗

arXiv2025

An Extension of the Iterated Moving Average

This work introduces an extension of the iterated moving average filter, called the Extended Kolmogorov-Zurbenko (EKZ) filter for time series and spatio-temporal analysis. The iterated application of a central simple moving average (SMA) filter, also known as a Kolmogorov-Zurbenko (KZ) filter, is a low-pass filter defined by the length of the moving average window and the number of iterations. These two arguments determine the filter properties such as the energy transfer function and cut-off frequency. However, the existing KZ filter is only defined for positive odd integer widow lengths. Therefore, for any finite time series dataset there is only a relatively small selection of possible window lengths, determined by the length of the dataset, with which to apply a KZ filter. This inflexibility impedes use of KZ filters for a wide variety of applications such as time series component separation, filtration, signal reconstruction, energy transfer function design, modeling, and forecasting. The proposed EKZ filter extends the KZ and SMA filters by permitting a widened range of argument selection for the filter window length providing the choice of an infinite number of filters that may be applied to a dataset, affording enhanced control over the filter characteristics and greater practical application. Simulations and real data application examples are provided.

stat.ME↗

arXiv2024

Predicting Coastal Water Levels in the Context of Climate Change Using Kolmogorov-Zurbenko Time Series Analysis Methods

Given recent increases in ocean water levels brought on by climate change, this investigation decomposed changes in coastal water levels into its fundamental components to predict maximum water levels for a given coastal location. The study focused on Virginia Key, Florida, in the United States, located near the coast of Miami. Hourly mean lower low water (MLLW) levels were obtained from the National Data Buoy Center from January 28, 1994, through December 31, 2023. In the temporal dimension, Kolmogorov-Zurbenko filters were used to extract long-term trends, annual and daily tides, and higher frequency harmonics, while in the spectral dimension, Kolmogorov-Zurbenko periodograms with DiRienzo-Zurbenko algorithm smoothing were used to confirm known tidal frequencies and periods. A linear model predicted that the long-term trend in water level will rise 2.02 feet from January 1994 to December 2050, while a quadratic model predicted a rise of 5.91 during the same period. In addition, the combined crests of annual tides, daily tides, and higher frequency harmonics increase water levels up to 2.16 feet, yielding a combined total of 4.18 feet as a lower bound and a combined total of 8.09 feet as an upper bound. These findings provide a foundation for more accurate prediction of coastal flooding during severe weather events and provide an impetus for policy choices with respect to residential communities, businesses, and wildlife habitats. Further, using Kolmogorov-Zurbenko analytic methods to study coastal sites throughout the world could draw a more comprehensive picture of the impact climate change is having on coastal waters globally.

physics.ao-ph↗

arXiv2024

Theoretical and Practical Limits of Signal Strength Estimate Precision for Kolmogorov-Zurbenko Periodograms with Dynamic Smoothing

This investigation establishes the theoretical and practical limits of signal strength estimate precision for Kolmogorov-Zurbenko periodograms with dynamic smoothing and compares them to those of standard log-periodograms with static smoothing. Previous research has established the sensitivity, accuracy, resolution, and robustness of Kolmogorov-Zurbenko periodograms with dynamic smoothing in estimating signal frequencies. However, the precision with which they estimate signal strength has never been evaluated. To this point, the width of the confidence interval for a signal strength estimate can serve as a criterion for assessing the precision of such estimates: the narrower the confidence interval, the more precise the estimate. The statistical background for confidence intervals of periodograms is presented, followed by candidate functions to compute and plot them when using Kolmogorov-Zurbenko periodograms with dynamic smoothing. Given an identified signal frequency, a static smoothing window and its smoothing window width can be selected such that its confidence interval is narrower and, thus, its signal strength estimate more precise, than that of dynamic smoothing windows, all while maintaining a level of frequency resolution as good as or better than that of a dynamic smoothing window. These findings suggest the need for a two-step protocol in spectral analysis: computation of a Kolmogorov-Zurbenko periodogram with dynamic smoothing to detect, identify, and separate signal frequencies, followed by computation of a Kolmogorov-Zurbenko periodogram with static smoothing to precisely estimate signal strength and compute its confidence intervals.

stat.AP↗

arXiv2024

Periodicity in New York State COVID-19 Hospitalizations Leveraged from the Variable Bandpass Periodic Block Bootstrap

The outbreak of the SARS-CoV-2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision-making in healthcare and public health domains. Investigating the presence, intensity, and precise nature of seasonal trends, as well as these temporal patterns, is essential for forecasting future occurrences, planning interventions, and making informed decisions based on the evolution of events over time. This study employs the Variable Bandpass Periodic Block Bootstrap (VBPBB) to separate and analyze different periodic components by frequency in time series data, focusing on annually correlated (PC) principal components. Bootstrapping, a method used to estimate statistical sampling distributions through random sampling with replacement, is particularly useful in this context. Specifically, block bootstrapping, a model-independent resampling method suitable for time series data, is utilized. Its extensions are aimed at preserving the correlation structures inherent in PC processes. The VBPBB applies a bandpass filter to isolate the relevant PC frequency, thereby minimizing contamination from extraneous frequencies and noise. This approach significantly narrows the confidence intervals, enhancing the precision of estimated sampling distributions for the investigated periodic characteristics. Furthermore, we compared the outcomes of block bootstrapping for periodically correlated time series with VBPBB against those from more traditional bootstrapping methods. Our analysis shows VBPBB provides strong evidence of the existence of an annual seasonal PC pattern in hospitalization rates not detectible by other methods, providing timing and confidence intervals for their impact.

stat.AP↗

arXiv2024

Seasonal and Periodic Patterns of PM2.5 in Manhattan using the Variable Bandpass Periodic Block Bootstrap

Air quality is a critical component of environmental health. Monitoring and analysis of particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) plays a pivotal role in understanding air quality changes. This study focuses on the application of a new bandpass bootstrap approach, termed the Variable Bandpass Periodic Block Bootstrap (VBPBB), for analyzing time series data which provides modeled predictions of daily mean PM2.5 concentrations over 16 years in Manhattan, New York, the United States. The VBPBB can be used to explore periodically correlated (PC) principal components for this daily mean PM2.5 dataset. This method uses bandpass filters to isolate distinct PC components from datasets, removing unwanted interference including noise, and bootstraps the PC components. This preserves the PC structure and permits a better understanding of the periodic characteristics of time series data. The results of the VBPBB are compared against outcomes from alternative block bootstrapping techniques. The findings of this research indicate potential trends of elevated PM2.5 levels, providing evidence of significant semi-annual and weekly patterns missed by other methods.

stat.AP↗

arXiv2024

Non-Parametric Estimation of Multiple Periodic Components in Turkey's Electricity Consumption

Electric generation and consumption are an essential component of contemporary living, influencing diverse facets of our daily routines, convenience, and economic progress. There is a high demand for characterizing the periodic pattern of electricity consumption. VBPBB employs a bandpass filter aligned to retain the frequency of a PC component and eliminating interference from other components. This leads to a significant reduction in the size of bootstrapped confidence intervals. Furthermore, other PC bootstrap methods preserve one but not multiple periodically correlated components, resulting in superior performance compared to other methods by providing a more precise estimation of the sampling distribution for the desired characteristics. The study of the periodic means of Turkey electricity consumption using VBPBB is presented and compared with outcomes from alternative bootstrapping approaches. These findings offer significant evidence supporting the existence of daily, weekly, and annual PC patterns, along with information on their timing and confidence intervals for their effects. This information is valuable for enhancing predictions and preparations for future responses to electricity consumption.

stat.AP↗

arXiv2024

Seasonal and Periodic Patterns in US COVID-19 Mortality using the Variable Bandpass Periodic Block Bootstrap

Since the emergence of the SARS-CoV-2 virus, research into the existence, extent, and pattern of seasonality has been of the highest importance for public health preparation. This study uses a novel bandpass bootstrap approach called the Variable Bandpass Periodic Block Bootstrap (VBPBB) to investigate the periodically correlated (PC) components including seasonality within US COVID-19 mortality. Bootstrapping to produce confidence intervals (CI) for periodic characteristics such as the seasonal mean requires preservation of the PC component's correlation structure during resampling. While existing bootstrap methods can preserve the PC component correlation structure, filtration of that PC component's frequency from interference is critical to bootstrap the PC component's characteristics accurately and efficiently. The VBPBB filters the PC time series to reduce interference from other components such as noise. This greatly reduces bootstrapped CI size and outperforms the statistical power and accuracy of other methods when estimating the periodic mean sampling distribution. VBPBB analysis of US COVID-19 mortality PC components are provided and compared against alternative bootstrapping methods. These results reveal crucial evidence supporting the presence of a seasonal PC pattern and existence of additional PC components, their timing, and CIs for their effect which will aid prediction and preparation for future COVID-19 responses.

stat.AP↗

arXiv2024

Periodically Correlated Time Series and the Variable Bandpass Periodic Block Bootstrap

This research introduces a novel approach to resampling periodically correlated (PC) time series using bandpass filters for frequency separation called the Variable Bandpass Periodic Block Bootstrap (VBPBB) and then examines the significant advantages of this new method. While bootstrapping allows estimation of a statistic's sampling distribution by resampling the original data with replacement, and block bootstrapping is a model-free resampling strategy for correlated time series data, both fail to preserve correlations in PC time series. Existing extensions of the block bootstrap help preserve the correlation structures of PC processes but suffer from flaws and inefficiencies. Analyses of time series data containing cyclic, seasonal, or PC principal components often seen in annual, daily, or other cyclostationary processes benefit from separating these components. The VBPBB uses bandpass filters to separate a PC component from interference such as noise at other uncorrelated frequencies. A simulation study is presented, demonstrating near universal improvements obtained from the VBPBB when compared with prior block bootstrapping methods for periodically correlated time series.

stat.ME↗

arXiv2020

Theoretical and Practical Limits of Kolmogorov-Zurbenko Periodograms with Dynamic Smoothing in Estimating Signal Frequencies

This investigation establishes the theoretical and practical limits of Kolmogorov-Zurbenko periodograms with dynamic smoothing in their estimation of signal frequencies in terms of their sensitivity, accuracy, resolution, and robustness. While the DiRienzo-Zurbenko algorithm performs dynamic smoothing based on local variation in a periodogram, the Neagu-Zurbenko algorithm performs dynamic smoothing based on local departure from linearity in a periodogram. This article begins with a summary of the statistical foundations for both the DiRienzo-Zurbenko algorithm and the Neagu-Zurbenko algorithm, followed by instructions for accessing and utilizing these approaches within the R statistical program platform. Brief definitions, importance, statistical bases, theoretical and practical limits, and demonstrations are provided for their sensitivity, accuracy, resolution, and robustness in estimating signal frequencies. Next using a simulated time series in which two signals close in frequency are embedded in a significant level of random noise, the predictive power of these approaches are compared to the autoregressive integral moving average (ARIMA) approach, with support again garnered for their being robust when data is missing. Throughout, the article contrasts the limits of Kolmogorov-Zurbenko periodograms with dynamic smoothing to those of log-periodograms with static smoothing, while also comparing the performance of the DiRienzo-Zurbenko algorithm to that of the Neagu-Zurbenko algorithm. It concludes by delineating next steps to establish the precision with which Kolmogorov-Zurbenko periodograms with dynamic smoothing estimate signal strength.

stat.AP↗

arXiv2018

Comment On the Connection Between Planets, Dark Matter and Cancer, by Hector Socas-Navarro (arXiv:1812.02482 [physics.med-ph])

In arXiv:1812.02482 Socas-Navarro (SN) provided multiple confirmation of the claimed 88 days melanoma periodicity. This greatly strengthens the observation by Zioutas and Valachovic (ZV). Here we comment on the work by SN, because it objects the interpretation of the observation by ZV. Notice that SN objection is based on serious assumptions, which were explicitly excluded by ZV. Further, the conclusion made with a sub-set of data (4 percent) is statistically not significant to dispute ZV. On the contrary, since the same periodicity appears also in other 8 major cancer types, we consider it as a global oscillatory behaviour of cancer. At this stage, such a rather ubiquitous cancer periodicity makes any discussion of a small subset of data at least secondarily. Further, we show here that the 88 days Melanoma periodicity is not related to solar activity. Planetary lensing of streaming low speed invisible massive particles remains the only viable explanation, as it has been introduced previously with a number of physics observations [4]. We also show that planetary lensing of low speed particles cannot be considered in isolation, because of the dominating Sun gravity, at least for the inner planets. Interestingly, gravitational lensing - deflection favours low speed particles.

physics.med-ph↗