In power analysis for multivariable Cox regression models, variance of the estimated log-hazard ratio for the treatment effect is usually approximated by inverting the expected null information matrix. Because in many typical power analysis settings assumed true values of the hazard ratios are not necessarily close to unity, the accuracy of this approximation is not theoretically guaranteed. To address this problem, the null variance expression in power calculations can be replaced with one of alternative expressions derived under the assumed true value of the hazard ratio for the treatment effect. This approach is explored analytically and by simulations in the present paper. We consider several alternative variance expressions, and compare their performance to that of the traditional null variance expression. Theoretical analysis and simulations demonstrate that while the null variance expression performs well in many non-null settings, it can also be very inaccurate, substantially underestimating or overestimating the true variance in a wide range of realistic scenarios, particularly those where the numbers of treated and control subjects are very different and the true hazard ratio is not close to one. The alternative variance expressions have much better theoretical properties, confirmed in simulations. The most accurate of these expressions has a relatively simple form - it is the sum of inverse expected event counts under treatment and under control scaled up by a variance inflation factor.

This dataset contains information about the percent variance between the actual and budgeted revenue (SD23 measure GTW.A.8). The City of Austin has numerous revenue sources, including charges for services/goods, taxes, and more. This measure helps provide insight about whether the City is receiving as much revenue as anticipated. For each revenue type and year, this dataset provides the budgeted revenue, actual revenue, and percent variance. This data comes from the City of Austin's Open Budget (Revenue Budget) application. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percent-Variance-Between-Actual-and-Budgeted-Reven/wmvj-b5er/

Analysis of ‘Strategic Measure_Percent Variance Between Actual and Budgeted Revenue’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/650f5191-ae12-4c45-8f14-effec5e32907 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains information about the percent variance between the actual and budgeted revenue (SD23 measure GTW.A.8). The City of Austin has numerous revenue sources, including charges for services/goods, taxes, and more. This measure helps provide insight about whether the City is receiving as much revenue as anticipated.

For each revenue type and year, this dataset provides the budgeted revenue, actual revenue, and percent variance. This data comes from the City of Austin's Open Budget (Revenue Budget) application.

View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percent-Variance-Between-Actual-and-Budgeted-Reven/wmvj-b5er/

--- Original source retains full ownership of the source dataset ---

R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.

In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).

In the second section, calculated the variance inflation factor (VIF).

In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).

In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.

NMI results on all the data sets.

We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM?$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our analysis of IBM returns. The models are compared with the test for superior predictive ability (SPA) and the reality check for data snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish good and bad models in our analysis.

Purity results on all the data sets.

The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of conditions.

The Waymo Open Dataset currently contains 1,950 segments. The authors plan to grow this dataset in the future. Currently the datasets includes:

1,950 segments of 20s each, collected at 10Hz (390,000 frames) in diverse geographies and conditions Sensor data 1 mid-range lidar 4 short-range lidars 5 cameras (front and sides) Synchronized lidar and camera data Lidar to camera projections Sensor calibrations and vehicle poses

Labeled data Labels for 4 object classes - Vehicles, Pedestrians, Cyclists, Signs High-quality labels for lidar data in 1,200 segments 12.6M 3D bounding box labels with tracking IDs on lidar data High-quality labels for camera data in 1,000 segments 11.8M 2D bounding box labels with tracking IDs on camera data

In this article, we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit, we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a one-step boosted forest. We show with simulated and real data that the one-step boosted forest has a reduced bias compared to the original random forest. The article also provides a variance estimate of the one-step boosted forest by an extension of the infinitesimal Jackknife estimator. Using this variance estimate, we can construct prediction intervals for the boosted forest and we show that they have good coverage probabilities. Combining the bias reduction and the variance estimate, we show that the one-step boosted forest has a significant reduction in predictive mean squared error and thus an improvement in predictive performance. When applied on datasets from the UCI database, one-step boosted forest performs better than random forest and gradient boosting machine algorithms. Theoretically, we can also extend such a boosting process to more than one step and the same principles outlined in this article can be used to find variance estimates for such predictors. Such boosting will reduce bias even further but it risks over-fitting and also increases the computational burden. Supplementary materials for this article are available online.

Script for calculate variance partition method and hierarchical partition method for scales regional and local

The provided dataset contains results from Monte Carlo simulations related to variance swaps. The data is organized into multiple sheets, each focusing on different parameters and scenarios.Figure 1:Monte Carlo Simulations: This section presents the results of Monte Carlo simulations for both discretely-sampled and continuously-sampled variance swaps. The values are reported for different sample sizes (N=12 to N=322), showing how the estimated variance swap values converge as the number of samples increases.Sample 1 and Sample 2: These represent two different sets of simulation results, each showing the impact of varying sample sizes on the variance swap values.Figure 2:κθ (Kappa Theta): This section explores the impact of different values of κθ on the variance swap values. θ̃ (Theta Tilde): This part examines the effect of varying θ̃ on the variance swap values .σθ (Sigma Theta): This section analyzes the influence of σθ on the variance swap values .θ₀ (Theta Zero): This part investigates the impact of different initial volatility levels (θ₀) on the variance swap values .Sheet 3:λ (Lambda): This section studies the effect of varying λ on the variance swap values .η (Eta): This part examines the influence of η on the variance swap values .v (Nu): This section analyzes the impact of v on the variance swap values .δ (Delta): This part investigates the effect of varying δ on the variance swap values .Overall, the dataset provides a comprehensive analysis of how different parameters and sampling methods affect the valuation of variance swaps, offering insights into the sensitivity and convergence behavior of these financial instruments under various conditions.

ACC results on all the data sets.

Abstract Arithmetic map operations are very common procedures used in GIS to combine raster maps resulting in a new and improved raster map. It is essential that this new map be accompanied by an assessment of uncertainty. This paper shows how we can calculate the uncertainty of the resulting map after performing some arithmetic operation. Actually, the propagation of uncertainty depends on a reliable measurement of the local accuracy and local covariance, as well. In this sense, the use of the interpolation variance is proposed because it takes into account both data configuration and data values. Taylor series expansion is used to derive the mean and variance of the function defined by an arithmetic operation. We show exact results for means and variances for arithmetic operations involving addition, subtraction and multiplication and that it is possible to get approximate mean and variance for the quotient of raster maps.

Data for a figure that presents the variance and covariance for a true random walk. There is a comparison shown between the numerical result from 1024 unique random walks and the analysis, by kinisi, of the same 1024 random walks.

Created using showyourwork from this GitHub repo.

The dataset originates from 10-year mid-range values (2013-2022) of sea temperature in the surface, the water masses (intervals for 5 m, 15 m, 30 m, 50 m, 100 m, 150 m, 200 m and 250 m), as well as at the seabed. The distance from the seabed goes with the current model layout from a few cm on shallow water and up to 1.5 m when the total depth is 100 m or more. Temperatures are given in degrees celcius. The data set is available as WMS and WCS services, as well as for download via the Institute of Marine Research’s Geoserver https://kart.hi.no/data – select Layer preview and search for the data set for multiple download options. The coastal model Norkyst (version 3) is a calculation model that simulates e.g. current, salinity and temperature with 800 meters spatial resolution, in several vertical levels and with high resolution in time for the entire Norwegian coast, based on the model system ROMS (Regional Ocean Modeling System, http://myroms.org). NorKyst is being developed by the Institute of Marine Research in collaboration with the Norwegian Meteorological Institute. https://imr.brage.unit.no/imr-xmlui/handle/11250/116053

No description is available. Visit https://dataone.org/datasets/d5e49b70e03436d772def3b3861d2de8 for complete metadata about this dataset.

The three datasets were collected to study the genetic architecture of sexual size dimorphism in the seed beetle Callosobruchus maculatus (Quantitative_genetics_data_set), the response of sexual dimorphism to artificial selection (artificial_selection_data_set) and to isolate and quantify the effect of Y haplotypes on male body size (Y_introgression_data_set).

Quantitative genetics: A four generation breeding design with pedigree information for 8022 individuals and body size measurements for 7356 individuals. The breeding design and sample size of the study allows to partition genetic variances into additive autosomal, additive sex-linked, autosomal dominance and X-linked dominance variance.

Artificial selection: Family level phenotypic data over 10 generations of artificial selection using direct progenitors of the quantitative genetics experiment in 5 different selection regimes (random selection [C], sexually antagonistic selection, for increased sexual dimorphism [SA], sex li...

Each file contains a 60x3385 data matrix of log10 expression measurements, scaled to unit variance within traits.NOTE: This dataset has been superseded by a more up to date version. View the current version here: https://doi.org/10.48610/a3c5652

Chen, Stacey H, (2008) "Estimating the Variance of Wages in the Presence of Selection and Unobservable Heterogeneity." Review of Economics and Statistics 90:2, 275-289.