Over the forecast period until 2029, the sales channel distribution share is forecast to exhibit fluctuations among the two segments. Only in the segment Online, a significant increase can be observed over the forecast period. In this segment, the sales channel distribution share exhibits a difference of **** percent between 2022 and 2029. Find further statistics on other topics such as a comparison of the number of users in Croatia and a comparison of the sales channel distribution share in Singapore. The Statista Market Insights cover a broad range of additional markets.

Estimate’s comparison between normal distribution (N) and t distribution (t).

In all the cases the distribution for the first member was demonstrated to be significantly greater that the one for the second member, except in the case of the parameter “Number of components” in which the first member of the comparison was significantly lower than the second one.

Middle income earners made up the largest share of both ************ and ********************** buyers in the United States in 2023. In the same period, upper income earners constituted the largest share of both *************** and ************ buyers.

Comparison of the estimates for the mean and variance of the normal distribution using Eq (15) versus maximum likelihood estimation, from 100 simulations with true value of μ = 0 and σ2 = 1.

Data_Description_ECOG-01477_Comparison of approaches to combine species distribution models based on different sets of predictorsHuman activities were described using different variables: water bodies (WR, LK, IMAR), population (HPd), distance to highways (Dhi) and land-cover. Land cover layers were processed so that the surface area of each class (PAST, SV, OAKM, CM, OAKW, CW, OLG, DHER, FT, IHER, LW, RW, HE, NM, VIN). Latitude and longitude were used as the spatial variables. Topography was represented by three variables: slope (S), degree of southward exposure (SE), and degree of westward exposure (WE), which were derived from digital elevation models. Climate variables (temperature and precipitation) were obtained from the datasets supplied by the Spanish Institute of Meteorology (Agencia Estatal de Meteorología).Data_Description_ECOG-01477.zip

Manufacturing and trade were the largest industries in Sweden based on share of employees, both with over ** percent of the registered number of employees. Even though companies within agriculture, forestry, and fishing made up ** percent of the registered companies, they held less than two percent of the number of employees.

Chotikapanich and Griffiths (Journal of Business and Economic Statistics, 2002, 20(2), 290-295) introduced the Dirichlet distribution to the estimation of Lorenz curves. This distribution naturally accommodates the proportional nature of income share data and the dependence structure between the shares. Chotikapanich and Griffiths fit a family of five Lorenz curves to one year of Swedish and Brazilian income share data using unconstrained maximum likelihood and unconstrained nonlinear least squares. We attempt to replicate the authors' results and extend their analyses using both constrained estimation techniques and five additional years of data. We successfully replicate a majority of the authors' results and find that some of their main qualitative conclusions also hold using our constrained estimators and additional data.

This statistic shows the distribution of online sales revenue in France between 2014 and 2018, by company size. It shows that enterprises with at least *** employees generated ** percent of the annual revenue in online sales in 2018.

The occurrence of species vulnerable to habitat fragmentation is likely to depend on the size and separation of the fragments. However, the shape of the function that relates occurrence to these landscape parameters may be affected by other factors that are less easily measured, in which case relationships with size and separation in one area may predict occurrence elsewhere only poorly. 2. We explored how well the distribution of red squirrels Sciurus vulgaris in fragmented woodlands was predicted by simple logistic regression models empirically derived in other fragmented landscapes. 3. Comparisons between predictions lead us to identify thresholds in fragment size (> 10 ha) and distance to a source (< 600 m) where the probability of squirrel occupancy was at least 0.9 in all landscapes. These values may reflect squirrel minimum habitat requirements for home range and dispersal in the worst study area. 4. For fragments < 10 ha (outside shared thresholds), models developed in a landscape could predict squirrel occupancy elsewhere only in 17% of cases, as other factors such as demography or habitat quality might become relevant in very small and isolated fragments. 5. The predictive ability for small fragments also improved when the range of fragment sizes in the area of observation fell within the range of sizes in the area where the model was developed. 6. Some models gave correct between-year predictions of squirrel distribution in the same landscape despite noticeable changes in regional squirrel population density. 7. When size and distance thresholds were met, we found that models could be used successfully elsewhere. In addition, threshold values indicate how large forest fragments should be and how they should be arranged to favour squirrel occurrence in a landscape. Palabras clave: Computer model, Habitat fragmentation, Population, Squirrel

Comparison of the power of the test for n = 2 and n = 3—Student’s t distribution.

These data contain all the raw results needed to support the conclusions for the final product. These data are water sampling locations (latitude and longitude), date of water sampling, quantitative PCR values for each water sample, and stream flow at USGS stream gauging stations on sampling day.

We were interested here in particular in conditions where un-modeled effects are present as manifested by the different degradation curve at 45°C. Although all algorithms were given the same amount of information to the degree practical, there were considerable differences in performance. Specifically, the combined Bayesian regression-estimation approach implemented as a RVM-PF framework has significant advantages over conventional methods of RUL estimation like ARIMA and EKF. ARIMA, being a purely data-driven method, does not incorporate any physics of the process into the computation, and hence ends up with wide uncertainty margins that make it unsuitable for long-term predictions. Additionally, it may not be possible to eliminate all non-stationarity from a dataset even after repeated differencing, thus adding to prediction inaccuracy. EKF, though robust against non-stationarity, suffers from the inability to accommodate un-modeled effects and can diverge quickly as shown. We did not explore other variations of the Kalman Filter that might provide better performance such as the unscented Kalman Filter. The Bayesian statistical approach, on the other hand, appears to be well suited to handle various sources of uncertainties since it defines probability distributions over both parameters and variables and integrates out the nuisance terms. Also, it does not simply provide a mean estimate of the time-to-failure; rather it generates a probability distribution over time that best encapsulates the uncertainties inherent in the system model and measurements and in the core concept of failure prediction.

Using kernel density estimation we describe the distribution of household size-adjusted real income and how it changed over the business cycle of the 1980s in the United States and the United Kingdom. We confirm previous studies that show income inequality increased in the two countries and the middle of the distribution was squashed down. Using a series of statistical tests, however, we find that while the mass in both tails of the distribution increased significantly in both countries over the period, by far the greatest gains were in the upper tail.

Comparison of the shape of the rapidity distribution of $\Lambda$ to various transport model versions.

Data and Code associated with the paper entitled "Hot to Model Intermittent Water Supply: Comparing Modelling Choices and Their Impact on Inequality" Description of contents and instructions on how to use this dataset are in the file README.md located in the root path

Second-degree dominance has become a widely accepted criterion for ordering distribution functions according to social welfare. However, it provides only a partial ordering, and it may fail to rank distributions that intersect. To rank intersecting distribution functions, we propose a general approach based on rank-dependent theory. This approach avoids making arbitrary restrictions or parametric assumptions about social welfare functions and allows researchers to identify the weakest set of assumptions needed to rank distributions according to social welfare. Our approach is based on two complementary sequences of nested dominance criteria. The first (second) sequence extends second-degree stochastic dominance by placing more emphasis on differences that occur in the lower (upper) part of the distribution. The sequences characterize two separate systems of nested subfamilies of rank-dependent social welfare functions. This allows us to identify the least restrictive rank-dependent social preferences that give an unambiguous ranking of a given set of distribution functions. We also provide an axiomatization of the sequences of dominance criteria and the corresponding subfamilies of social welfare functions. We show the usefulness of our approach using two empirical applications; the first assesses the welfare implications of changes in household income distributions over the business cycle, while the second performs a social welfare comparison of the actual and counterfactual outcome distributions from a policy experiment.

Provide the public with monthly statistics on interbank remittance transactions by region (Financial Information Company).

Amphidecta calliomma is a butterfly species that occurs in Colombia, Bolivia, Peru, Venezuela, Ecuador, Panama and Brazil (in the states of Mato Grosso, Mato Grosso do Sul, Rondônia and Pará). Here, we present a new occurrence of A. calliomma in the Carajás National Forest (Pará, eastern Amazon), expanding the known distribution of the species. We also provide Species Distribution Model comparing the contribution of the new occurrence to species area of occurrence projections, supporting future field research. The projections reveal an expansion of area of occurrence for A. calliomma located mainly in the southeast portion of Amazon Forest. Despite its wide distribution, the small number of records of A. calliomma may indicate that the species has a low detectability in surveys. This study provides support for new surveys and reduces the knowledge gap about A. calliomma, thus supporting its conservation. Methods Sampling From 05 to 14 November 2019, we conducted a campaign to collect frugivorous butterflies in the Carajás National Forest (southwestern Pará state, Brazil). Butterflies were collected using Van Someren-Rydon traps baited with a mixture of banana and beer (instead of sugarcane), which was fermented for 48 hours, following methodologies adapted from Uehara-Prado et al. (2005) and Freitas et al. (2014). The individuals captured in the traps were collected (SISBIO license number: 68977-1) and identified based on literature resources and with the help of the website “Butterflies of America” (https://www.butterfliesofamerica.com/L/Nymphalidae.htm, accessed in November 2020) (Warren et al., 2013). After identification and preparation, the specimen of A. calliomma was incorporated into the entomological collection of the Museu Paraense Emílio Goeldi (MPEG.HLE 04045043) (MPEG, Pará, Brazil). Occurrence records In addition to field collection, we retrieved data from Global Biodiversity Information Facility (GBIF; www.gbif.org, accessed in November 2022; DOI: https://doi.org/10.15468/dl.kgbph8) and SpeciesLink (https://specieslink.net/, accessed in November 2022) and from published articles, totaling 52 records. We also removed duplicate and non-georeferenced data. We removed inconsistencies using a conservative pipeline (Gomes et al., 2018). Thus, our final database totaled 16 occurrence records (11 from the digital databases, 4 from articles and 1 occurrence from our field collections) (Supporting Information Table 1). Climate information We downloaded climate data with a resolution of 10 arc-minutes (~ 18 km x 18 km) from the WorldClim database version 2.1 (www.worldclim.org, accessed in November 2022). We focused on non-correlated climate data, based on ecological relevance. Butterflies are highly sensitive to climate as warm temperatures can stimulate their flight muscles efficiency and wind is a key component for flying animals and precipitation affects species richness (Turner et al. 1987; Checa et al. 2019). We downloaded and tested for correlation (coefficient threshold |ρ| < 0.7) seven historical climate variables: precipitation, water vapor pressure, solar radiation, wind speed, maximum temperature, minimum temperature and average temperature. Species Distribution Model We used an algorithm based on maximum entropy (MaxEnt) to produce models of species potential distribution to estimate A. calliomma area of occurrence (AOO) (Phillips et al., 2004; IUCN, 2022). We followed Gomes et al. (2019) and used background information to calibrate MaxEnt predictions based on data of tree species from Amazon forest since most of the occurrences of the A. calliomma are located in this biome. Background data is a sample from the study area used to characterize its environmental conditions (Phillips et al., 2009). Distribution modelling methods using background data generally outperformed those using presence-absence or pseudo-absence information, especially when modelling mobile species (Fernandez et al., 2022). Also, background information methods are more flexible, producing more realistic and less over-fitted predictions (Peterson et al., 2011). Since A. calliomma has little occurrence information available, we used a more flexible approach to understand the general distribution pattern of the species. We used product, threshold and hinge features of MaxEnt (Boucher-Lalonde et al., 2012; Merow et al., 2013). To evaluate the models, we used a null model approach (Raes & Steege, 2007). We tested the predictive performance of the A. calliomma models as estimated by the area under the ROC curve (AUC) against the predictive performance of 99 null models generated using the same number of occurrences of A. calliomma generated randomly. If the AUC of the models scores higher than the 95th best null models, this means that the chance of a model generated randomly showing a better performance is less than five percent. The models were converted in binary maps by using the 10th percentile training presence threshold, which omits the regions with environmental suitability lower than the lowest 10% of occurrence records (Gomes et al., 2018). We then clipped the binary maps by using the extent of occupancy (EOO) of the species plus a buffer of 300 km, based on the notion that the EOO is restricted by dispersal capabilities (Gaston, 2009; De Ro et al., 2021). We estimated A. calliomma AOO using the new occurrence sampled and comparing with the AOO estimation with no new occurrence. All calculations and analyses were performed with R version 3.6.3, including the R packages raster (Hijmans & van Etten, 2016), rgdal (Bivand, Keitt, & Rowlingson, 2017), gstat (Pebesma & Heuvelink, 2016), dismo (Hijmans et al., 2016), rJava (Urbanek, 2017) and SDMTools (VanDerWal et al., 2019).