This table shows overall ATCEMS response interval performance for entire fiscal years. Data in the table is broken out by incident response priority and service area (City of Austin or Travis County).

Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.

The 2020 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF50) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Census Disclosure Avoidance System (DAS) into the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile.

The PPMF0 was the source of the publicly released, official 2020 Census data products referenced above, and was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2020 Census Edited File (CEF) as the initial input; the official location for the PPMF0 is on the United States Census Bureau FTP server, but we also include a copy of it here for convenience. The replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input.

Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach worked well.

The PPMF0 and PPMFi files contained here are provided so that external researchers can estimate properties of DAS-introduced error without privileged access to internal USCB-curated data sets; further information on the estimation methodology can be found in Ashmead et. al 2024.

The 2020 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY22-DSEP-004). The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile for both the United States and Puerto Rico, and include geographic detail down to the Census Block level. They do not include attributes specific to either the Detailed DHC-A or Detailed DHC-B products; in particular, data on Major Race (e.g., White Alone) is included, but data on Detailed Race (e.g., Cambodian) is not included in the PPMF0 and replicates.

Data on the median length of time (in months) between the first and second born offspring of a family in the United Kingdom (UK) from 2005 to 2019 shows that throughout this period the average length of time in between births remained fairly stable with the exception of 2005, 2011, 2012 and 2016 whereby the median increased by one month.

We present a Wilson interval for binomial proportions for use with multiple imputation for missing data. Using simulation studies, we show that it can have better repeated sampling properties than the usual confidence interval for binomial proportions based on Rubin’s combining rules. Further, in contrast to the usual multiple imputation confidence interval for proportions, the multiple imputation Wilson interval is always bounded by zero and one. Supplementary material is available online.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier. The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided. This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

Abstract A calculator program has been written to give confidence intervals on branching ratios for rare decay modes (or similar quantities) calculated from the number of events observed, the acceptance factor, the background estimate and the associated errors. Results from different experiments (or different channels from the same experiment) can be combined. The calculator is available in http://www.slac.stanford.edu/~barlow/limits.html.

Title of program: syslimit Catalogue Id: ADQN_v1_0

Nature of problem Calculating confidence intervals for a Poisson mean based on observed data, with uncertainties in efficiencies and backgrounds.

Versions of this program held in the CPC repository in Mendeley Data ADQN_v1_0; syslimit; 10.1016/S0010-4655(02)00588-X

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

This paper considers identification and estimation of a fixed-effects model with an interval-censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the error disturbances can be heteroskedastic over cross-sectional units as long as they are stationary within a cross-sectional unit; the semiparametric model also allows for serial correlation of the error disturbances. A conditional-logit-type composite likelihood estimator is proposed for the logistic fixed-effects model, and a composite maximum-score-type estimator is proposed for the semiparametric model. In general, the scale of the coefficient parameters is identified by these estimators, meaning that the causal effects of interest are estimated directly in cases where the latent dependent variable is of primary interest (e.g., pure data-coding situations). Monte Carlo simulations and an empirical application to birthweight outcomes illustrate the performance of the parametric estimator.

Supplementary MaterialData filesFigures A1 - A8: Simulations BoxplotsFigures B1: B16: Application Dendrograms Software: R and Matlab

Government Effectiveness: Percentile Rank, Upper Bound of 90% Confidence Interval in Tanzania was reported at 45.28 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Tanzania - Government Effectiveness: Percentile Rank, Upper Bound of 90% Confidence Interval - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Abstract A Fortran 77 routine has been developed to calculate confidence intervals with and without systematic uncertainties using a frequentist confidence interval construction with a Bayesian treatment of the systematic uncertainties. The routine can account for systematic uncertainties in the background prediction and signal/background efficiencies. The uncertainties may be separately parametrized by a Gauss, log-normal or flat probability density function (PDF), though since a Monte Carlo approach...

Title of program: pole version 1.0 Catalogue Id: ADTA_v1_0

Nature of problem The problem is to calculate a frequentist confidence interval on the parameter of a Poisson process with known background in the presence of systematic uncertainties in experimental parameters such as signal efficiency or background prediction.

Versions of this program held in the CPC repository in Mendeley Data ADTA_v1_0; pole version 1.0; 10.1016/j.cpc.2003.12.002

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)

A recent pair of articles published in the journal Evolution presented a test for assessing the validity of hierarchical macroevolutionary models. The premise of the test is to compare numerical point estimates of parameters from two levels of analysis; if the estimates differ, the hierarchical model is purportedly flawed. The articles in question (Meyer and Wiens 2017; Meyer et al. 2018) apply their proposed test to BAMM, a scientific software program that uses a Bayesian mixture model to estimate rates of evolution from phylogenetic trees. The authors use BAMM to estimate rates from large phylogenies (n > 60 tips) and they apply the method separately to subclades within those phylogenies (median size: n = 3 tips); they find that point estimates of rates differ between these levels and conclude that the method is flawed, but they do not test whether the observed differences are statistically meaningful. There is no consideration of sampling variation and its impact at any level of their analysis. Here, I show that numerical differences across groups that they report are fully explained by a failure to account for sampling variation in their point estimates. Variance in evolutionary rate estimates – from BAMM and all other methods – is an inverse function of clade size; this variance is extreme for clades with 5 or fewer tips (e.g., 70% of clades in the focal study). The articles in question rely on negative results that are easily explained by low statistical power to reject their preferred null hypothesis, and this low power is a trivial consequence of high variance in their point estimates. I describe additional mathematical and statistical mistakes that render the proposed testing framework invalid on first principles. Evolutionary rates are no different than any other population parameters we might wish to estimate, and biologists should use the training and tools already at their disposal to avoid erroneous results that follow from the neglect of variance.

This repository contains the input data associated with the research paper titled "The Effects of PLU Polling Interval on the Reconstructed OD Matrix: A Dutch Case Study Using a Data-Driven Method". The paper is published in the TRR. The input_data.rar includes all relevant raw data. This data has the overall purpose of understanding the effect of temporal resolution of data on the estimated origin destination matrix. The 1.experienced_plans.xml file contains the input data from MATSim with the travel diaries of users in Amsterdam. amsterdamMezuroZones.rar contains the geographical information of the data used from MATSiM . The files inside can be opened in python.

This dataset provides information about the number of properties, residents, and average property values for Interval Street cross streets in Chantilly, VA.

With the rapid development of data acquisition and storage space, massive datasets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To accommodate such big volume in the analysis, a variety of methods have been proposed in the circumstances of complete or right censored survival data. However, existing development of big data methodology has not attended to interval-censored outcomes, which are ubiquitous in cross-sectional or periodical follow-up studies. In this work, we propose an easily implemented divide-and-combine approach for analyzing massive interval-censored survival data under the additive hazards model. We establish the asymptotic properties of the proposed estimator, including the consistency and asymptotic normality. In addition, the divide-and-combine estimator is shown to be asymptotically equivalent to the full-data-based estimator obtained from analyzing all data together. Simulation studies suggest that, relative to the full-data-based approach, the proposed divide-and-combine approach has desirable advantage in terms of computation time, making it more applicable to large-scale data analysis. An application to a set of interval-censored data also demonstrates the practical utility of the proposed method.

The Cades Cove 40 ft Interval Contour Lines is the primary Cades Cove 40 ft Interval Contour Line data product produced and distributed by the National Park Service, Great Smoky Mountains National Park.

Includes accelerometer data using an ActiGraph to assess usual sedentary, moderate, vigorous, and very vigorous activity at baseline, 6 weeks, and 10 weeks. Includes relative reinforcing value (RRV) data showing how participants rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes data on the breakpoint, or Pmax of the RRV, which was the last schedule of reinforcement (i.e. 4, 8, 16, …) completed for the behavior (exercise or sedentary). For both Pmax and RRV score, greater scores indicated a greater reinforcing value, with scores exceeding 1.0 indicating increased exercise reinforcement. Includes questionnaire data regarding preference and tolerance for exercise intensity using the Preference for and Tolerance of Intensity of Exercise Questionnaire (PRETIEQ) and positive and negative outcome expectancy of exercise using the outcome expectancy scale (OES). Includes data on height, weight, and BMI. Includes demographic data such as gender and race/ethnicity. Resources in this dataset:Resource Title: Actigraph activity data. File Name: AGData.csvResource Description: Includes data from Actigraph accelerometer for each participant at baseline, 6 weeks, and 10 weeks.Resource Title: RRV Data. File Name: RRVData.csvResource Description: Includes data from RRV at baseline, 6 weeks, and 10 weeks, OES survey data, PRETIE-Q survey data, and demographic data (gender, weight, height, race, ethnicity, and age).

Presents the distribution of TOTAL, SINGLETON AND MULTIPLE births for 2014 by Interval in Years Since Last Birth. This table only includes women having second and subsequent births. Primiparous wome (i.e. women who have had no previous pregnancy resulting in a live birth or stillbirth) are not included in this table. This table outlines data for total births, live births, stillbirths, early neonatal deaths and perinatal mortality rates, as well as presenting the number of maternities. The Perinatal Statistics Report 2014 is a report on national data on Perinatal events in 2014. Information on every birth in the Republic of Ireland is submitted to the National Perinatal Reporting System (NPRS). All births are notified and registered on a standard four part birth notification form (BNF01) which is completed where the birth takes place. Part 3 of this form is sent to the HPO for data entry and validation. The information collected includes data on pregnancy outcomes (with particular reference to perinatal mortality and important aspects of perinatal care), as well as descriptive social and biological characteristics of mothers giving birth. See the complete Perinatal Statistics Report 2014 at http://www.hpo.ie/latest_hipe_nprs_reports/NPRS_2014/Perinatal_Statistics_Report_2014.pdf

Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval in Burundi was reported at 9.9057 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Burundi - Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.