Statistical Information Research Articles

Beyond the incident: Influences on the perception of multiple instances of discrimination.

This article seeks to enhance the theoretical understanding of discrimination perception, especially in contexts with relevant statistical information. Previous research has provided important insights into the perception of single, ambiguous instances of discrimination. However, the generalizability of these insights to scenarios involving multiple, repeated instances of discrimination remains unclear. The current research aims to reduce this uncertainty by investigating whether three key determinants of discrimination perception in single instances - perpetrators' prototypicality, victims' control, and system-justifying beliefs (SJB) - also influence observers' perceptions of repeated discrimination. In Experiments 1 and 2, participants perceived stronger discrimination when perpetrators were prejudiced members of the advantaged group. In Experiments 3 and 4, perceived discrimination intensified when victims had low control, a trend significantly noted in tabular presentations but not in sequential ones. In Experiments 5 and 6, a negative correlation between SJB and discrimination perception was observed. The theoretical and practical implications of these results, as well as open questions, are discussed. Overall, these studies advance our knowledge of discrimination perception in multiple instances. They highlight the intricate interplay between statistical data, moral judgements, and individual belief systems, paving the way for a more nuanced exploration of the underlying psychological processes of discrimination perception.

Home- and community-based care in the new generation of Medicaid administrative data

AbstractThis study aimed to assess the quality of Home and Community-Based Services (HCBS) data elements within the Transformed Medicaid Statistical Information System Analytical File (TAF) and to devise strategies for improving its research usability. Analyzing Medicaid TAF data from 2016 to 2018, we conducted a retrospective data quality analysis, focusing specifically on HCBS-related data elements. Through secondary data analysis, we identified significant challenges, including considerable missingness and inconsistencies that hamper the effective use of TAF for research purposes. Despite these issues, we developed three approaches that enabled us to identify 94% of known 1915(c) waiver claims as HCBS. Our study also revealed considerable cross-state variations in data quality, prompting specific recommendations for utilizing HCBS data within TAF. Ultimately, the study concludes that while challenges exist, applying our recommended strategies can yield data of acceptable quality for most states, particularly in identifying HCBS usage and classifying them into service categories. Given the growing importance of home-based care, there is a pressing need to prioritize improvements in TAF’s HCBS data quality to better inform policy and practice.

A multi-dimensional analysis of corporate blogs

Abstract Using a corpus-based multi-dimensional analysis, this study investigates the linguistic features and variation of corporate blogs from four industries: Service industry, the Wholesale and Retail Trade industry, the Manufacturing industry, and Information industry. The primary goal of this study is to examine the linguistic variation in corporate blogs from different industries. The data used for the current study is a 570,745-word corpus consisting of 995 textual posts from 40 top-ranked corporate blogs. In the multi-dimensional analysis, 67 linguistic features in the corpus are tagged, counted and normalized by using a Multidimensional Analysis Tagger (MAT). We imported the output files containing relevant statistical information from the MAT and then to SPSS 25.0 for a further t-test analysis. Overall, the corporate blogs are closest to the text type of general narrative exposition. The study finds that, as a hybrid genre, corporate blogs are basically informationally dense, non-narrative, interactive, and contextually independent. There are some statistically significant differences between four sub-corpora in corporate blogs. In particular, the Wholesale & Retail Trade sub-corpus is the most distinctive one that is significantly different from others on many dimensions, characterized by giving instructions, or telling stories, rather than persuading their audiences to do something.

Determination of dynamic collapse limit states using the energy-based method for multi-story RC frames subjected to column removal scenarios

The Alternative Load Path method is widely adopted to perform progressive collapse analyses for reinforced concrete (RC) frame structures subjected to column removal scenarios. Considering that the progressive collapses are usually dynamic phenomena, nonlinear dynamic analyses can yield the most accurate results. However, the associated computational cost is high. Alternatively, the energy-based method (EBM) can be employed to efficiently calculate the maximum dynamic responses. Unfortunately, collapse or dynamic limit states (DLS) cannot be directly determined when the EBM is adopted for the progressive collapse analyses, because it is essentially a quasi-static approach. To tackle this issue, in this paper an EBM-Intersection Point-based (EBM-IP-based) method is proposed to effectively determine the DLS. The method is considering that the dynamic instability will occur once the dynamic capacity curve exceeds the corresponding static capacity curve. Hence, it is possible to determine the DLS by making a correction on the intersection point of the aforementioned two curves without performing nonlinear dynamic time-history analyses. Compared with the other energy components, the kinetic energy at the moment of the peak displacement response is relatively small and the effectiveness of the EBM is verified. Through both static pushdown analyses and incremental dynamic analyses for 48 column removal scenarios of six different multi-story RC frames, the effectiveness of the proposed EBM-IP-based method is verified and the associated coefficients are calibrated. Statistical information and (joint) probabilistic density functions (PDF) of the coefficients are identified. Further, stochastic analyses are carried out in order to quantify the model uncertainties when adopting the EBM-IP-based method. On the basis of all the calculation results, a Gumbel distribution is adopted to capture the PDF of the model uncertainty in relation to displacements, while a lognormal distribution is adopted to fit the PDF of the model uncertainty in terms of resistances. Moreover, a multi-variate Gaussian distribution model with two components is employed to fit the joint PDF.

Semantically Interoperable Census Data: Unlocking the Semantics of Census Data Using Ontologies and Linked Data

The Canadian Census of Population is a survey that collects statistical information on the Canadian population. These censuses contain valuable socioeconomic data that is often used by both the public and private sectors for project planning and decision-making. However, there are a few issues that may arise when using census data. Firstly, data wrangling, which is often a time-consuming process, needs to be conducted in order to clean and prepare the data for integration and use. Secondly, different datasets across different census years may be using different terms to describe the same concept/entity, hence creating a problem of referential equivalence (i.e., how do we know whether two different datasets are referring to the same concepts/entities?). Lastly, the data found in a census is often described using natural language that isn't easily interpreted by machines and can be difficult to break down or deconstruct. In this paper, we develop and propose the use of an ontology for representing the data from the Canadian Census of Population as linked data in order to address the aforementioned issues, evaluate the ontology using competency questions based on real world use cases, and discuss the advantages of census linked data for integration and visualisation uses.

СТАН МОСТОВОГО ГОСПОДАРСТВА УКРАЇНИ НА ДОРОГАХ ЗАГАЛЬНОГО КОРИСТУВАННЯ ДЕРЖАВНОГО ЗНАЧЕННЯ НА ПІДКОНТРОЛЬНИХ ТЕРИТОРІЯХ ЗА 2023 РІК

Introduction. Bridges are important critical infrastructure facilities; they play a key role in economic development and ensure the security of regions and the state as a whole. During the period of martial law, bridges became an important element of the country's defense capability, as well as routes for humanitarian evacuation and export logistics. To make effective management decisions at the level of the State Agency for the Restoration and Development of Infrastructure of Ukraine in the field of bridge management, the Analytical Expert Bridge Management System (AESUM) software package is being implemented. Problem statement. In connection with the introduction of martial law in Ukraine, ensuring the proper functioning of the bridge sector has become of great social importance, since the availability of sustainable transport links allows to ensure the vital activity of the population and the functioning of the state under martial law. It is also important to improve the strategy of operation of transport facilities, taking into account the scientific and analytical apparatus of the AESUM software package. Objective. Implementation of an effective strategy for the operation of bridges on highways in order to plan financial resources for their repair and maintenance. Providing the general public with up-to-date statistical information on bridges from AESUM will be useful for scientific and practical purposes, and identifying certain analytical trends will allow to improve AESUM in terms of forming a strategy for the operation of transport facilities. Materials and methods. Analytical, statistical, system analysis. Results. The paper presents analytical and statistical information on transport facilities on public roads of national importance in the controlled territories of Ukraine as of the end of 2023. Certain trends have been identified and the technical condition of transport facilities has been focused on.

АНАЛІЗУВАННЯ ВПЛИВУ ЗМІНИ КРИТЕРІЮ ВИЯВЛЕННЯ МІСЦЬ (ДІЛЯНОК) КОНЦЕНТРАЦІЇ ДОРОЖНЬО-ТРАНСПОРТНИХ ПРИГОД НА БЕЗПЕКУ АВТОМОБІЛЬНИХ ДОРІГ

Introduction. Ensuring road safety is one of the foremost tasks in any country with vehicular traffic. Despite significant efforts made to improve road safety in Ukraine and globally, the issue of traffic accidents is still relevant. Problem statement. Given the relatively high level of mortality due to accidents, Ukraine needs to ensure highly qualified management of road safety, which requires an integrated approach and consideration of various aspects, including legislation, infrastructure, vehicles and road user’s behavior. Purpose. Ensuring road safety, reducing the severity of the consequences of traffic accidents and providing comfortable travel conditions for road users. Materials and methods. Current legislative acts and regulatory documents regarding the identification of places (areas) of concentration of traffic accidents, as well as statistical information on the number of such places (areas) were analyzed. Results. It has been established that the implementation of a new criterion for identifying the places (areas) of the concentration of traffic accidents is justified and indicates a systematic approach to road safety management.

Structural Changes in the Dynamics of China’s Exports and Imports

The economic policy pursued has turned China into a major trading power. In terms of GDP, the Chinese economy has been the first economy in the world for a number of recent years; China’s rapidly growing trade flows have made it the largest trading partner for many countries. Over the past twenty years there has been rapid growth in exports and imports. The article puts forward a hypothesis about the presence of structural instability in the dynamics of Chinese exports for the period from 2000 to 2020. The hypothesis was confirmed and two periods were identified in the dynamics of exports and imports with different natures of the main development trend: from 2000 to 2008 and from 2009 to 2020. For each stage, trend equations were selected that describe the dynamics of exports and imports and an interval forecast of indexes for 2021 was made. Materials and methods. During the work, dynamic, structural analysis of analytical and statistical information was used; methods of analytical, logical, systemic, correlation and regression analysis were used, as well as analysis of structural changes. The analysis was carried out using the Statistica 10.0 program.Results. A change in the value of the import coverage ratio by exports was revealed before and after structural changes in the dynamics of the main foreign economic indexes and an assessment of their statistical significance was given. An analysis of the influence of export dynamics on GDP dynamics was carried out and it was found that before structural changes, the influence of export dynamics on GDP dynamics was statistically insignificant, and after a structural change, export dynamics began to have a direct impact on changes in China’s GDP. Conclusion. Analysis of official statistical data on the main indexes of China’s foreign economic activity for the period from 2000 to 2020 allowed us to note that the volume of Chinese exports increased over the period 2010-2019 from $1.58 trillion in 2010 (10.34% of global exports), to $2.50 trillion in 2019 (12.81% of global exports). In 2019, China continued to rank first in terms of export value among all countries in the world. Despite the challenging year of 2020, China’s exports reached almost 2.6 trillion US dollars and increased by 4% compared to 2019.China also occupies a leading position in the export of high-tech goods - their volume is 731.9 billion US dollars, the share of exports in the global volume is 25%. At the same time, the dynamics of Chinese exports for the period from 2000 to 2020 was heterogeneous and characterized by structural instability. Two stages can be distinguished with different characteristics of the main development trend: from 2000 to 2008 and from 2009 to 2020. Based on this, the authors came to the conclusion that to describe the main trend in the development of export and import dynamics for the period from 2000 to 2008, the exponential model is best suited. To describe the main trend in the development of export and import dynamics in the period from 2009 to 2020 - logarithmic model. A study of the consistency of changes in export and import volumes showed that in the period from 2009 to 2020 was characterized by greater consistency in China’s main foreign trade flows than the period from 2000 to 2008. In the period before the structural change in the dynamics of exports and imports, the coverage ratio varied from 105.7% to 127.6%; in the period after the structural change, the coverage ratio changed from 108.9% to 135.4%.Checking the statistical significance of the differences in the coefficient of coverage of imports by exports before and after the structural change showed that the differences are not statistically significant. In the period from 2000 to 2008 the dynamics of China’s exports did not have a statistically significant impact on the dynamics of the country’s GDP. From 2009 to 2020 after structural changes in the nature of the dynamics of the main indexes of China’s foreign trade, changes in exports began to have a statistically significant impact on the dynamics of GDP. A 1% increase in China’s exports now results in a 0.25% increase in GDP.The practical significance of the paper is determined by the developed methodology for analyzing structural changes in the dynamics of exports and imports, examined using the example of China.

Black-White Disparities in Asthma Hospitalizations and ED Visits Among Medicaid-Enrolled Children.

Asthma is a common, potentially serious childhood chronic condition that disproportionately afflicts Black children. Hospitalizations and emergency department (ED) visits for asthma can often be prevented. Nearly half of children with asthma are covered by Medicaid, which should facilitate access to care to manage and treat symptoms. We provide new evidence on racial disparities in asthma hospitalizations and ED visits among Medicaid-enrolled children. We used comprehensive Medicaid claims data from the Transformed Medicaid Statistical Information System. Our study population included 279 985 Medicaid-enrolled children with diagnosed asthma. We identified asthma hospitalizations and ED visits occurring in 2019. We estimated differences in the odds of asthma hospitalizations and ED visits for non-Hispanic Black versus non-Hispanic white children, adjusting for sex, age, Medicaid eligibility group, Medicaid plan type, state, and rurality. In 2019, among Black children with asthma, 1.2% had an asthma hospitalization and 8.0% had an asthma ED visit compared with 0.5% and 3.4% of white children with a hospitalization and ED visit, respectively. After adjusting for other characteristics, the rates for Black children were more than twice the rates for white children (hospitalization adjusted odds ratio 2.45, 95% confidence interval 2.23-2.69; ED adjusted odds ratio 2.42; 95% confidence interval 2.33-2.51). There are stark racial disparities in asthma hospitalizations and ED visits among Medicaid-enrolled children with asthma. To diminish these disparities, it will be important to implement solutions that address poor quality care, discriminatory treatment in health care settings, and the structural factors that disproportionately expose Black children to asthma triggers and access barriers.

EXPRESS: Revisiting representativeness heuristic classic paradigms: Replication and extensions of nine experiments in Kahneman and Tversky (1972).

Kahneman and Tversky (1972) showed that when people make probability judgments, they tend to ignore relevant statistical information (e.g., sample size) and instead rely on a representativeness heuristic, whereby subjective probabilities are influenced by the degree to which a target is perceived as similar to (representative of) a typical example of the relevant population, class or category. Their paper has become a cornerstone in many lines of research and has been used to account for various biases in judgment and decision-making. Despite the impact this article has had on theory and practice, there have been no direct replications. In a pre-registered experiment (N = 623; Amazon MTurk on CloudResearch), we conducted a replication and extensions of nine problems from Kahneman and Tversky (1972). We successfully replicated eight out of the nine problems. We extended the replication by examining the consistency of heuristic responses across problems and by examining decision style as a predictor of participants' use of the representativeness heuristic. Materials, data, and code are available on: https://osf.io/nhqc4/.

Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills.

Visual statistical Learning (SL) allows infants to extract the statistical relationships embedded in a sequence of elements. SL plays a crucial role in language and communication competencies and has been found to be impacted in Autism Spectrum Disorder (ASD). This study aims to investigate visual SL in infants at higher likelihood of developing ASD (HL-ASD) and its predictive value on autistic-related traits at 24-36 months. At 6 months of age, SL was tested using a visual habituation task in HL-ASD and neurotypical (NT) infants. All infants were habituated to a visual sequence of shapes containing statistically predictable patterns. In the test phase, infants viewed the statistically structured, familiar sequence in alternation with a novel sequence that did not contain any statistical information. HL-ASD infants were then evaluated at 24-36 months to investigate the associations between visual SL and ASD-related traits. Our results showed that NT infants were able to learn the statistical structure embedded in the visual sequences, while HL-ASD infants showed different learning patterns. A regression analysis revealed that SL ability in 6-month-old HL-ASD infants was related to social communication and interaction abilities at 24-36 months of age. These findings indicate that early differences in learning visual statistical patterns might contribute to later social communication skills.

Optimal Decoding Order and Power Allocation for Sum Throughput Maximization in Downlink NOMA Systems.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels.

Statistics as an Essential Element of Modern Hybrid Warfares

The relevance of the study lies in the fact that at present, information flows, often based on “falsification” of data, are having an increasing influence. And statistics act as such a tool.Purpose of the study. The purpose of this paper is to study opinions and approaches to the use of statistics to conduct information wars.Materials and methods. The study examined the main directions of statistics as a tool for working with data. The most common and practically justified is the use of statistics as a tool of knowledge, management, propaganda and control. The result of interpreting statistical data primarily depends on the degree of objectivity and level of literacy of those who work with the data: both at the initial stage of the original data array, and at the stage of explaining the resulting values.Results. It was revealed that the same parameters (proportions) can be interpreted in completely different ways. Such differences arise both from the subjectivity of the perception of these data and from the degree of elaboration of the material. As a result, a field for discrepancies and discussion arises.Conclusion. Statistics have already become an integral component of wars of a new format, where the battle for people’s worldview becomes the basis for further presence in many spheres of life. But with competent, properly structured and timely statistical information, it is possible to suppress falsely generated data and inflict damage on the enemy that significantly exceeds direct military clashes on the battlefield.

Economic growth in the Sub-Saharan African world region: convergence theory

Purpose The processes of convergence are particularly challenging in the Sub-Saharan African frameworks, considering the diversity of contexts and endogenous particularities. Creating conditions to support these nations to improve their socioeconomic dynamics and performance requires additional contributions from international organisations, governments and the scientific community. In this scenario, this paper aims to analyse the convergence process in Sub-Saharan African countries over the past three decades. Design/methodology/approach To achieve these objectives, data from the World Bank were considered for the gross domestic product (GDP) per capita over the period 1990–2021. This statistical information was assessed through panel data approaches based on the models from the convergence theory. Specifically, the concepts of sigma and beta convergence were addressed, as well as the concept of catch-up rates. Findings The findings obtained highlight evidence of the existence of clubs of convergence among the Sub-Saharan African countries and the processes of catching up. These results may be relevant support for the policymakers and international funds and programmes. Originality/value This research provides a new perspective on the convergence of GDP per capita in Sub-Saharan African countries, based on an analysis focused on groups of countries identified on the basis of catch-up rates. This approach presents a way of dealing with the different specificities of these nations.

RD-OpenMax: Rethinking OpenMax for Robust Realistic Open-Set Recognition.

Open-set recognition (OSR) toward a practical open-world setting has attracted increasing research attention in recent years. However, existing OSR settings are either too idealized or focus on specific scenes such as long-tailed distribution and few-shot samples, which fail to capture the complexity of real-world scenarios. In this article, we propose a realistic OSR (ROSR) setting that covers a diverse range of challenging and real-world scenarios, including fine-grained cases with strong semantic correlation and a large number of species, few-shot samples, long-tailed sample distribution, dynamic inputs (e.g., images, spatio-temporal, and multimodal signals) and cross-domain adaptation. In particular, we rethink the simple and basic OpenMax for the ROSR setting and introduce a novel method, regularized discriminative OpenMax (RD-OpenMax), to handle the challenges in the ROSR setting. RD-OpenMax improves upon the basic OpenMax approach by introducing a covariance attention-based covariance pooling (CACP) module as a global aggregation step before the deep architecture's classifier. This module explores rich statistical information on features and provides discriminative distance scores for OpenMax. To address the instability of extreme value theory (EVT) estimation due to insufficient training samples under few-shot and long-tailed scenarios, we propose a regularized EVT (REVT) method based on Monte Carlo sampling to recalibrate the distribution of distance scores. As such, our RD-OpenMax performs a REVT model of distance scores generated by discriminative CACP representations to distinguish known classes and recognize unknown ones effectively and robustly. Extensive experiments are conducted on more than ten visual benchmarks across several scenarios, and the empirical comparisons show that the ROSR setting challenges existing state-of-the-art OSR approaches. Moreover, our RD-OpenMax clearly outperforms its counterparts under the ROSR setting while performing favorably against state-of-the-arts under the traditional OSR setting.

Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

Extracting cyclostationary features from vibration signals is one of the most effective approaches in bearing fault diagnosis. However, current methods require prior knowledge of cycle-frequencies or other statistical information, which constrains their applicability across various scenarios. In this paper, we introduce a novel dual adaptive filtering method, incorporating cycle-frequency estimation to solve the existing problem. The method firstly employs an adaptive line enhancer (ALE) to isolate the first-order cyclostationary signal, thereby the cycle-frequencies can be effectively detected using an exhaustive estimation technique. Subsequently, an adaptive frequency-shift (FRESH) filter is further applied to extract the second-order cyclostationary features from the residual components. The proposed method successfully overcomes the challenge of separating cyclostationary signals without prior knowledge and can be tailored to real-time application scenarios. Besides, the approach distinguishes between the two cyclostationary signal types, effectively resolving any aliasing concerns inherent in their statistical characteristics. The effectiveness of the method is verified through simulation, experiments, and engineering data analysis. It is demonstrated that the method significantly enhances diagnostic accuracy and is more suitable for early fault diagnosis of rolling bearings by estimating spectral coherence on the extracted signals.

ADAM optimised human speech emotion recogniser based on statistical information distribution of chroma, MFCC, and MBSE features

ADAM optimised human speech emotion recogniser based on statistical information distribution of chroma, MFCC, and MBSE features

A VISION-BASED SYSTEM DESIGN AND IMPLEMENTATION FOR ACCIDENT DETECTION AND ANALYSIS VIA TRAFFIC SURVEILLANCE VIDEO

This study aims to test the entire framework on an AI demo board and investigate the issue of automatically and effectively recognizing and analyzing traffic incidents captured by surveillance cameras. To begin, the motion interaction field (MIF) method, which is capable of detecting collisions in video, is used to locate damaged automobiles based on the interactions of various moving objects. Second, the location of the destroyed vehicles is determined using the YOLO v3 model. Using a hierarchical clustering method, the vehicle trajectories prior to the collision are recovered, and the related trajectories are reconstructed. Finally, a perspective transformation is used to project the trajectory into a vertical view to help traffic cops make better decisions. The unbiased finite impulse response (UFIR) method, which does not require statistical information about the external noise, is used to estimate the vehicle's velocity. The estimated velocity and impact angle from the vertical view can then be used to investigate the traffic accident. Finally, a Huawei AI demo board called HiKey970, which was used to code all of the aforementioned algorithms, is used in an experiment to show how useful and effective the proposed method is in practice. A few mishap observation recordings are sent onto the demo board. Mishaps are recognized and the proper vehicle directions are gathered.

Analysis of the Relationship Between Tourists Experience, Knowledge, Digital Strategic Decisions of Tourism Site Managers and Tourism Business Sustainability

The aim of this research is to propose a tourism observatory design to support strategic decision-making in tourist destinations. The proposed methodology uses input-output analysis. We propose the destination survey as a model for randomly selecting visitors at tourist attractions. The research results reveal that the observatory concept serves as a viable tool for tourism policy, addressing the information gaps at the destination level, where national statistical operations fall short. Observatories act as management tools that generate and concentrate scientific and statistical information, becoming the main source of official tourist data about a destination. However, in almost provinces, existing information does not fully meet the needs of users. Therefore, this research suggests a tourism observatory model to enhance the availability of systematic and timely information for decision-making at the provincial level. We hope that provinces and territorial units on a sub-national scale, lacking a complete tourism information system, will find the proposed model useful. We expect them to implement tourism observatory projects as a means to address specific needs, resolve local issues by uniting various tourism system participants, and address deficiencies in their current tourism statistics system. In this regard, provincial tourism observatories are expected to play a decisive role in the development of indicators and the calculation of trends, and the results obtained will provide feedback on the actions taken in relation to tourism policy.

Efficient Verilog-A based time-domain variability analysis method for passive photonic integrated circuits

The performance of silicon photonic integrated circuits (PICs), especially wavelength filters, can be highly sensitive to variations in the fabrication process due to the large refractive index contrast of the silicon on insulator platform. This paper proposes an easy-to-implement and efficient time-domain variability analysis method for passive PICs. The method utilizes the polynomial chaos expansion technique to construct Verilog-A based models for estimating the statistical information of stochastic passive PICs. In comparison to existing methods, this approach is considerably easy to implement, efficient, and exhibits superior scalability, particularly as the numbers of ports and random parameters in the studied PICs increase. The technique is demonstrated via the time-domain variability analysis of a ring-resonator-based wavelength filter and a Mach-Zehnder interferometer-based demultiplexer filter.