Bootstrap Version Research Articles

Rancang Bangun Layanan Pengaduan Wisata Berbasis Web Dengan Pendekatan UML Pada DINBUDPAR Rembang

Rembang Regency has approximately 39 tourist attractions, where these tourist spots were found to have numerous visitors each month in 2023. This is crucial in boosting the country's foreign exchange earnings. However, public service management at the Rembang Regency Department of Culture and Tourism still employs conventional methods perceived as inefficient and time-consuming, especially in the complaint handling service. Whereas complaints services can be a forum for tourists or the surrounding community to contribute in providing complaints about the tourist attractions they visit. Therefore, researchers leverage technological advancements to design a web-based tourism complaint service information system for the relevant department. Using UML modeling method, CodeIgniter framework version 3, Bootstrap version 4.4.1, PHP programming language, HTML, CSS, JavaScript, and MySQL for database connectivity, this research aims to be implemented so that the department can efficiently manage complaint services and respond promptly.

Specifications tests for count time series models with covariates

AbstractWe propose a goodness-of-fit test for a class of count time series models with covariates which includes the Poisson autoregressive model with covariates (PARX) as a special case. The test criteria are derived from a specific characterization for the conditional probability generating function, and the test statistic is formulated as a $$L_2$$ L 2 weighting norm of the corresponding sample counterpart. The asymptotic properties of the proposed test statistic are provided under the null hypothesis as well as under specific alternatives. A bootstrap version of the test is explored in a Monte–Carlo study and illustrated on a real data set on road safety.

Digitally improving UK police surveillance and incidence response using real-time crowd reporting app: Digipolice

Overtime, evolving technologies has made ways of monitoring and controlling crime and action in the United Kingdom (UK) become more challenging which needs optimization with the help of technologies. Also with the rate of crime across cities in the UK like mass shootings, murder and gang gun battles, the UK Police system within localities are overwhelmed or may not efficiently cope with crime alerts and incidences coming in at increasing frequencies. However looking in the area of real time crowd reporting, there is need to leverage technology to enhance public safety, improve incident response, and foster community engagement. This necessitated the aim of this study to develop a digital policing surveillance app for crowd reporting in the UK. By utilizing digital policing surveillance apps, the aim is to improve crowd management capabilities by enabling real-time reporting of incidents and observations from both the public and law enforcement personnel. This was positioned to facilitate better situational awareness, efficient resource allocation, and effective response strategies. To achieve the objectives, Python Programming Language 3.8 were deployed together with a Django Framework and a Microsoft Visual Studio Build Tools. In the DP system, the Model-View-Controller (MVC) pattern, which divides an application into three interconnected components—the model, the view, and the controller—was used for the architectural design of the system. For the database, Django's ORM makes it simple to support many databases, and in this project straightforward SQLite3 database were utilised which is ideal for small-footprint applications. Using WebSockets to establish real-time, bidirectional communication between web clients and servers, the user interface and intelligence board for the system were developed. A clean designed frontend design system with Bootstrap version 5—an open-source, reusable design framework created by Twitter—was selected for its easy user interface. The system was put through 50 concurrent load tests with actual users swarming the app since the DP app was subjected to hundreds of people complaining in real time. 2. The host computer had a Core i5 vPro CPU running at 4GHz and 4GB of RAM. The result shows that 50 users successfully generated an average of 29.7 requests per second. The system scaled up nicely to 50 users and reached its highest response time of 2600 milliseconds (2.6 seconds) in total. A dramatic rise in activity and a longer response time occurred after there were more than 50 users. The system could not operate at its peak efficiency with 100 users because response times were inconsistent and exceeded 7000 milliseconds (7 seconds) for each request. The effectiveness of the online deployment was evaluated with respectable results, and the system requirements also underwent successful testing. The appropriate investigation of ethical issues was done to meet requirements. DigiPolice has the potential to develop into a cutting-edge programme for real-time crowd surveillance and incident reporting. UK Police can employ it to as advancement to its “My Police” with as fast-track real-time and collaborative crowd reporting enhancements.

Resampling-based confidence intervals and bands for the average treatment effect in observational studies with competing risks

The g-formula can be used to estimate the treatment effect while accounting for confounding bias in observational studies. With regard to time-to-event endpoints, possibly subject to competing risks, the construction of valid pointwise confidence intervals and time-simultaneous confidence bands for the causal risk difference is complicated, however. A convenient solution is to approximate the asymptotic distribution of the corresponding stochastic process by means of resampling approaches. In this paper, we consider three different resampling methods, namely the classical nonparametric bootstrap, the influence function equipped with a resampling approach as well as a martingale-based bootstrap version, the so-called wild bootstrap. For the latter, three sub-versions based on differing distributions of the underlying random multipliers are examined. We set up a simulation study to compare the accuracy of the different techniques, which reveals that the wild bootstrap should in general be preferred if the sample size is moderate and sufficient data on the event of interest have been accrued. For illustration, the resampling methods are further applied to data on the long-term survival in patients with early-stage Hodgkin’s disease.

Simulation comparison of modified confidence intervals based on robust estimators for coefficient of variation: skewed distributions case with real applications

In this article, we propose confidence intervals for the population coefficient of variation based on some robust estimators such as trimmed mean, winsorized mean, Hodges-Lehmann estimator and trimean. The proposed confidence intervals and their bootstrap versions were compared with the existing confidence intervals for the population coefficient of variation. The performances of the proposed confidence intervals were evaluated via a Monte-Carlo simulation study by considering the coverage probability, average width, standard deviation of widths, and coefficient of variation of widths as comparison criteria. The proposed confidence intervals performed well in terms of coverage probability on symmetrical distributions. In the case of skewed distributions, they were closer to the nominal confidence level and had narrower widths than the others. As a result, we proposed to use confidence intervals based on the Hodges-Lehmann estimator and winsorized mean for small sample sizes ( n = 10 and 15 ) and larger sample sizes for skewed distributions, respectively. The real-life datasets were analyzed to support the simulation results and confirm the practical applications of the proposed confidence intervals.

Design a Web-Based Library Information System Using the Waterfall Method (Case Study of Sma Muhammadiyah 2)

Information technology is a technology that is developing rapidly at this time. Computers as student learning media have many functions such as audio, animation, visual, and interactive capabilities. The main problem in the field of education is the low interest in reading students and reading habits. The library is expected to help increase students' interest in reading and reading habits. SMA Muhammadiyah has a new library and is still manual in recording borrowing and returning books. This makes it difficult for librarians to search for data and is at risk of losing books. It is difficult for students to find book information and the irregular placement of books makes the process of borrowing books ineffective and inefficient. Therefore, a web-based library information system is needed to make it easier for students to borrow books. The Waterfall method will be used to help design the library system at SMA Muhammadiyah 2 Cipondoh. The Waterfall method is a method that is very commonly used in the early stages of the manufacturing process. Application features developed by researchers at the Muhammadiyah 2 Cipondoh High School library include logins, book master data, book loan transaction data, book return transaction data, and book borrowing reports. Library information system development uses the PHP CodeIgniter 3 framework. The front ends used are HTML 5, CSS 3, and Bootstrap version 4.

All the Same? Different Measures of Personality Functioning Are Similar but Distinct. A Comparative Study from a Psychodynamic Perspective Using Exploratory Graph Analysis

Personality functioning (PF) is a central construct in many theories of personality pathology. Based on psychodynamic theories, two screening questionnaires to assess PF are widely used: The Inventory of Personality Organization—16 item version and the Operationalized Psychodynamic Diagnosis—Structure Questionnaire Short Form. This study aimed to explore the similarities and differences of the two questionnaires in a large clinical sample of N = 1636 psychotherapeutic inpatients. Correlation analyses were conducted to examine the associations between the global scores and between the subscales. The study further used Exploratory Graph Analysis (EGA) to explore the dimensionality of the items. The stability of estimates was evaluated using a bootstrap version of EGA (bootEGA). The results indicated that the two questionnaires are highly correlated, yet not multicollinear, and moderate to large correlations were found between their subscales. EGA revealed six dimensions that fairly represented the original subscales. BootEGA showed that the dimensions and items were stable, except for one item that did not load sufficiently on any dimension. The findings suggest that although the questionnaires are highly correlated, their subscales tap into distinct domains of PF. We discuss implications stemming from these findings for clinical and scientific practice.

Network Psychometrics of the 10-Item Perceived Stress Scale Among Patients With High Cardiovascular and Type 2 Diabetes Risk Using Exploratory Graph Analysis.

No studies have explored the internal structure of the 10-item Perceived Stress Scale in patients with high cardiovascular and diabetes risk. We scrutinized the dimensionality of the scale in this patient group using exploratory graph analysis, a technique within the developing field of network psychometrics. Analyses were conducted on 200 primary care patients. A bootstrap version of exploratory graph analysis assessed the stability of the dimensions based on structural consistency, item stability, and network loadings. Exploratory graph analysis revealed a 2-dimensional structure; structural consistency of the first dimension was high (0.863), whereas that for the second was low (0.667). Items belonging to the latter dimension did not cluster consistently with each other (ie, low item stability) and were not strongly associated with any particular dimension (ie, weak network loadings). Exploratory graph analysis offers unique outputs, making it easy to assess the dimensional integrity of scales. Further research is warranted regarding the second dimension of the Perceived Stress Scale.

TVMM: an R package for testing hypothesis on mean vectors

TThe Multivariate Tests for the Vector of Means (TVMM) package has six functions: T2O function, which computes Hotelling original’s T² test; T2Boot function, which computes the parametric bootstrap version of the original T2 test; T2RobustBoot function, which computes the robust parametric bootstrapversion of the T2Boot test; LRTTrace function, which computes the asymptotic version of the likelihood ratio test (LRT) using the trace operator; LRTTBoot function, which computes the parametric bootstrap version of the LRTTrace and the LRTTRBoot, which computes the robust version of the LRTTBoot. The alternative test versions of the LRT have the advantage of being valid for high-dimensional data. We describe the methods and illustrate the use of the TVMM package with real data on soil properties.

The validity of bootstrap testing for threshold autoregression

We consider bootstrap-based testing for threshold effects in non-linear threshold autoregressive (TAR) models. It is well-known that classic tests based on asymptotic theory tend to be biased in case of small, or even moderate sample sizes, especially when the estimated parameters indicate non-stationarity, or in presence of heteroskedasticity, as often witnessed in the analysis of financial or climate data. To address the issue we propose a supremum Lagrange Multiplier test statistic (sLM), where the null hypothesis specifies a linear autoregressive (AR) model against the alternative of a TAR model. We consider both the classical recursive residual i.i.d. bootstrap (sLMi) and a wild bootstrap (sLMw), applied to the sLM statistic, and establish their validity under the null hypothesis. The framework is new, and requires the proof of non-standard results for bootstrap analysis in time series models; this includes a uniform bootstrap law of large numbers and a bootstrap functional central limit theorem. The Monte Carlo evidence shows that the bootstrap tests have correct empirical size even for small samples; the wild bootstrap version (sLMw) is also robust against the presence of heteroskedasticity. Moreover, there is no loss of empirical power when compared to the asymptotic test and the size of the tests is not affected if the order of the tested model is selected through AIC. Finally, we use our results to analyse the time series of the Greenland ice sheet mass balance. We find a significant threshold effect and an appropriate specification that manages to reproduce the main non-linear features of the series, such as the asymmetric seasonal cycle, the main periodicities, and the multimodality of the probability density function.

Sistem inventaris berbasis web di workshop Teknik Elektro

This inventory information system provides several facilities that can be used by the manager, including: input of item data, delete item search item, and item data inventory report, item inventory system at the Electrical Engineering Workshop - STTR CEPU aims to facilitate the laboratory manager in data collection goods, both complementary goods or consumables, which are web-based, for example inputting new goods or damaged goods, can be done online, and the number of goods will be automatically entered in the database, without us adding up the goods manually, search results for item data more easily fast, accurate, and the results of reports that can be downloaded in PDF type, and can be printed. The purpose of the research is to increase efficiency in managing computerized laboratory inventory so that it can support the performance of lab managers, Archives and Electrical Engineering workshops, Designing this application using language programming PHP, CSS, HTML, Windows 10 64 bit Operating System, Visual Studio Code software version 1.69.1, Xampp Server version 1.7.7, PHP version 7.3.2, Bootstrap version 5.0, Google Chrome, 000webhost. The test results show that 77% of this application can run and make it easier for the Cepu-Sttr Laboratory manager.
 Keywords: Inventory System, Laboratory, Cepu Sttr

Optimal Subsampling Bootstrap for Massive Data

The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties. However, the vanilla version of bootstrap is no longer feasible computationally for many modern massive datasets due to the need to repeatedly resample the entire data. Therefore, several improvements to the bootstrap method have been made in recent years, which assess the quality of estimators by subsampling the full dataset before resampling the subsamples. Naturally, the performance of these modern subsampling methods is influenced by tuning parameters such as the size of subsamples, the number of subsamples, and the number of resamples per subsample. In this article, we develop a novel hyperparameter selection methodology for selecting these tuning parameters. Formulated as an optimization problem to find the optimal value of some measure of accuracy of an estimator subject to computational cost, our framework provides closed-form solutions for the optimal hyperparameter values for subsampled bootstrap, subsampled double bootstrap and bag of little bootstraps, at no or little extra time cost. Using the mean square errors as a proxy of the accuracy measure, we apply our methodology to study, compare and improve the performance of these modern versions of bootstrap developed for massive data through numerical study. The results are promising.

Use of Exploratory Graph Analysis in Inspecting the Dimensionality of the Revised University of California Los Angeles (R-UCLA) Loneliness Scale Among Older Adults.

Exploratory graph analysis (EGA) based on network theory has been introduced as a highly reliable and effective method to assess scales' dimensionality. We estimated the dimensional network structure of the Revised University of California Los Angeles Loneliness Scale using EGA among a cross-sectional cohort of Korean older adults living alone (N = 1,041). We also evaluated the stability of estimates using a bootstrap version of EGA (bootEGA) and verified the overall fit structure using confirmatory factor analysis (CFA). EGA revealed a two-dimensional structure of the scale initially. The bootEGA result revealed that Item 4 ("I do not feel alone") did not sufficiently load on any dimension, and Item 20 ("There are people I can turn to") was replicated in two or more dimensions. Removing these items resulted in better stability of the dimensions, leading to excellent structural consistency. CFA confirmed a satisfactory fit of the improved structure. [Research in Gerontological Nursing, 16(1), 15-20.].

A distance-based test of independence between two multivariate time series

We contribute to recent research on distance correlation by extending its capability to test for independence between two time series. The proposed test is a Portmanteau-type test based on double-centered distance cross-covariances. We show that the test statistic constructed in this way is asymptotically normal and pivotal under the null hypothesis of independence, and it is consistent under fixed alternative hypotheses of dependence. We also propose a fast wild bootstrap procedure that can significantly improve the performance of the asymptotic test in finite samples. An extensive Monte Carlo simulation study designed to compare the proposed test with other existing procedures shows that both the proposed test and its bootstrap version can achieve a good size and power performance for several data generating processes (DGPs), and they can significantly outperform other tests under model misspecification. An empirical application to illustrate the applicability of the proposed method is also given.

Final Project Registration Management System (SIMANTA) [Case Study : University of Muhammadiyah Sidoarjo

Muhammadiyah University of Sidoarjo is one of the private universities that provides final assignments and theses as one of the graduation requirements for obtaining Diploma and Bachelor degrees. The Final Assignment Registration Management System (SIMANTA) makes it easy for lecturers, students and admins to manage thesis data and provide information about the stages of the final project and thesis procedure for students. The development of this system was carried out to evaluate the existing system, namely SIMS UMSIDA. The previous system was still less than optimal in the procedures carried out in the implementation of the thesis because it was only used for the informatics study program. Therefore, with SIMANTA it can be used for all study programs and can distinguish data from each study program. The system design method in this study uses the prototype method and the data collection stage uses observation and interview techniques. The technology used for the SIMANTA design uses the Codeigniter version 4 framework, the PHP programming language version 7.4 and Bootstrap version 4x. So that the results of this study, the final assignment registration management system (SIMANTA) aims to provide convenience, manage data and provide information about thesis for lecturers, admins, and students at Muhammadiyah University of Sidoarjo.

Using a National Burn Registry to Develop a Model for Risk-Adjusted Length of Stay Benchmarking.

Length of stay (LOS) is a frequently reported outcome after a burn injury. LOS benchmarking will benefit individual burn centers as a way to measure their performance and set expectations for patients. We sought to create a nationwide, risk-adjusted model to allow for LOS benchmarking based on the data from a national burn registry. Using data from the American Burn Association's Burn Care Quality Platform, we queried admissions from 7/2015 to 6/2020 and identified 130,729 records reported by 103 centers. Using 22 predictor variables, comparisons of unpenalized linear regression and Gradient boosted (CatBoost) regressor models were performed by measuring the R2 and concordance correlation coefficient on the application of the model to the test dataset. The CatBoost model applied to the bootstrapped versions of the entire dataset was used to calculate O/E ratios for individual burn centers. Analyses were run on 3 cohorts: all patients, 10-20% TBSA, >20% TBSA. The CatBoost model outperformed the linear regression model with a test R2 of 0.67 and CCC of 0.81 compared with the linear model with R2=0.50, CCC=0.68. The CatBoost was also less biased for higher and lower LOS durations. Gradient-boosted regression models provided greater model performance than traditional regression analysis. Using national burn data, we can predict LOS across contributing burn centers while accounting for patient and center characteristics, producing more meaningful O/E ratios. These models provide a risk-adjusted LOS benchmarking using a robust data source, the first of its kind, for burn centers.

34 Using a National Burn Registry to Develop a Model for Risk-adjusted Length of Stay Benchmarking

IntroductionLength of stay (LOS) is a frequently reported outcome after a burn injury. Previous literature estimates LOS at 1 day per % total burn surface area (TBSA) but this varies considerably across patients & centers. LOS benchmarking will benefit individual burn centers as a way to measure their performance & set expectations for patients. We sought to create a nationwide, risk-adjusted model to allow for LOS benchmarking based on data from a national burn registry.MethodsUsing data from a national burn registry, we queried admissions from 7/2015-6/2020 & identified 126,129 records with LOS data reported by 103 centers. We selected 23 predictor variables on the basis of completeness (min. 75% required) & clinical significance. Missing data were multiply imputed with a Bayesian Ridge Regression estimator. All statistics were calculated in Python using Numpy & Scikit-Learn libraries. Comparisons of unpenalized linear regression & Gradient boosted (CatBoost) regressor models were performed by measuring the R2 & concordance correlation coefficient (CCC) on the application of the model to the test dataset. The CatBoost model applied to bootstrapped versions of the entire dataset was then used to calculate O/E ratios for individual burn centers. Confidence intervals (CI) for O/E ratios were calculated using a normal distribution parametric model. Analyses were run on 3 cohorts: all patients, 10-20% TBSA, >20% TBSA.ResultsThe CatBoost model outperformed the linear regression model with a test R2 of 0.68 & CCC of 0.81 compared to the regression model with R2=0.52, CCC=0.70. The CatBoost was also less biased for higher & lower LOS durations. Due to the CatBoost model’s superiority in predicting the outcome, this model alone was used for O/E ratio calculations. The O/E ratio data from the model for all 3 cohorts are shown in Figure 1.ConclusionsGradient boosted regression models provided greater model performance than traditional regression analysis. Using national burn data, we can predict LOS across contributing burn centers while accounting for patient & center characteristics, producing more meaningful O/E ratios. These models provide a risk-adjusted LOS benchmarking using a robust data source, the first of its kind, for burn centers.

Fourier-type tests of mutual independence between functional time series

We suggest tests of independence between time series of functional observations. The tests are based on characteristic functions which are appropriately estimated from functional observations. The limit distribution of the new test statistic is obtained under the null hypothesis, while under alternatives it is shown that the same test statistic almost surely diverges as the sample size increases. Since the limit null distribution is complicated, a bootstrap version of the test is suggested to assess the test’s performance in finite samples. Also, an application illustrates the use of the method with real data from financial markets. Extension to tests of mutual independence for multiple time series is also considered.

Evolution of the housing market under the framework of adaptive market hypothesis and martingale difference hypothesis: a case of India

PurposeThe purpose of this study to evaluate the evolving market efficiency of the housing market under the framework of adaptive market hypothesis and martingale difference hypothesis taking a case of India.Design/methodology/approachThe study used a wild bootstrap version of the generalized spectral (GS) test in the rolling window framework to measure possible time-varying linear and non-linear dependence in the housing market.FindingsThe study finds that the Indian housing market, in general, is not efficient, and this efficiency is dynamic, which changes with time lending support to the adaptive market hypothesis. The study confirms that the evolutionary model of individuals adapting to a changing environment via behavioural biases affects the efficiency of the housing market, which leads to the evolving efficiency of the housing market prices.Research limitations/implicationsThe study believes that the potential implications go beyond evolutionary forces and the adaptive market hypothesis , which, does not only depend on an individual's decision-making process but also on social psychology. Thus, a further attempt in this line, taking into account the social psychology and quantitative rigour towards drivers of evolving efficiency is suggested for future research.Practical implicationsThe study suggests that there is a possibility of extra returns for market players, but not always. The Indian housing market has witnessed several landmark reforms in recent years, so it is believed that these reforms would decrease the inefficiency level of this market. Contrary to this, the study’s findings reveal an increase in the inefficiency level in recent years. As the Indian housing market shows evolving efficiency, it is believed that the increased inefficiency is temporary. The increased inefficiency can be regarded as the settlement stage of the various policy and technical reforms.Originality/valueConfirming the presence or absence of adaptive efficiency in the housing market under possible non-linear dependence will be a significant addition to the existing literature.

Omnibus goodness of fit test based on quadratic distance

This study considers a goodness of fit test based on the quadratic distance (QD) in composite hypotheses. Lindsay et al. [Quadratic distances on probabilities: a unified approach. Ann Statist. 2008;36:983–1006] established a general theory of QD measures for the goodnees of fit test. Using the spectral decomposition of centred kernels, they verified that the QD test asymptotically follows a sum of weighed chi-square distributions. In this study special attention is paid to a smoothing kernel-based QD test and its bootstrap version. Their performances are compared via Monte Carlo simulations with those of the Bickel-Rosenblatt test and those of the Fisher's dispersion test for the normality and the testing for the Poisson distribution in IID samples and AR(1) models. The comparison results demonstrate the validity of our method.