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Topic Review
Statistical Methods for Food Composition Database Analysis
A food composition database (FCDB) or nutrient database is a compilation of the chemical composition of food and beverage items, obtained from chemical analyses, estimations from published literature, or unpublished laboratory reports. A summary of the statistical methods that have been directly applied to food composition databases and datasets is described here.
  • 957
  • 08 Jun 2022
Topic Review
Statistical Evidence Use of p-value
This paper addresses the use and misuse of p-value issue and review latest literature on the use of p-value in social and natural sciences.  
  • 524
  • 14 Jan 2021
Topic Review
Sports Analytics
Sports analytics are a collection of relevant, historical, statistics that can provide a competitive advantage to a team or individual. Through the collection and analyzation of these data, sports analytics inform players, coaches and other staff in order to facilitate decision making both during and prior to sporting events. The term "sports analytics" was popularized in mainstream sports culture following the release of the 2011 film, Moneyball, in which Oakland Athletics General Manager Billy Beane (played by Brad Pitt) relies heavily on the use of analytics to build a competitive team on a minimal budget. There are two key aspects of sports analytics — on-field and off-field analytics. On-field analytics deals with improving the on-field performance of teams and players, including questions such as "which player on the Red Sox contributed most to the team's offense?" or "who is the best wing player in the NBA?", etc. Off-field analytics deals with the business side of sports. Off-field analytics focuses on helping a sport organization or body surface patterns and insights through data that would help increase ticket and merchandise sales, improve fan engagement, etc. Off-field analytics essentially uses data to help rightsholders take decisions that would lead to higher growth and increased profitability. As technology has advanced over the last number of years data collection has become more in-depth and can be conducted with relative ease. Advancements in data collection have allowed for sports analytics to grow as well, leading to the development of advanced statistics and machine learning, as well as sport specific technologies that allow for things like game simulations to be conducted by teams prior to play, improve fan acquisition and marketing strategies, and even understand the impact of sponsorship on each team as well as its fans. Another significant impact sports analytics have had on professional sports is in relation to sport gambling. In depth sports analytics have taken sports gambling to new levels, whether it be fantasy sports leagues or nightly wagers, bettors now have more information at their disposal to help aid decision making. A number of companies and webpages have been developed to help provide fans with up to the minute information for their betting needs.
  • 896
  • 14 Nov 2022
Topic Review Peer Reviewed
Spatial Hurst–Kolmogorov Clustering
The stochastic analysis in the scale domain (instead of the traditional lag or frequency domains) is introduced as a robust means to identify, model and simulate the Hurst–Kolmogorov (HK) dynamics, ranging from small (fractal) to large scales exhibiting the clustering behavior (else known as the Hurst phenomenon or long-range dependence). The HK clustering is an attribute of a multidimensional (1D, 2D, etc.) spatio-temporal stationary stochastic process with an arbitrary marginal distribution function, and a fractal behavior on small spatio-temporal scales of the dependence structure and a power-type on large scales, yielding a high probability of low- or high-magnitude events to group together in space and time. This behavior is preferably analyzed through the second-order statistics, and in the scale domain, by the stochastic metric of the climacogram, i.e., the variance of the averaged spatio-temporal process vs. spatio-temporal scale.
  • 1.1K
  • 14 Apr 2022
Topic Review
Six Stages to Choose Sampling Techniques
In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases. Thus, there is a need to select a sample. The entire set of cases from which researcher sample is drawn in called the population. Since, researchers neither have time nor the resources to analysis the entire population so they apply sampling technique to reduce the number of cases. There are six stages to choose sampling techniques.
  • 93.8K
  • 17 Jul 2023
Topic Review
SIGKDD
SIGKDD is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining. It became an official ACM SIG in 1998.
  • 378
  • 18 Nov 2022
Topic Review
Population Ecology
Population ecology is a sub-field of ecology that deals with the dynamics of species populations and how these populations interact with the environment, such as birth and death rates, and by immigration and emigration. The discipline is important in conservation biology, especially in the development of population viability analysis which makes it possible to predict the long-term probability of a species persisting in a given patch of habitat. Although population ecology is a subfield of biology, it provides interesting problems for mathematicians and statisticians who work in population dynamics.
  • 645
  • 19 Oct 2022
Topic Review
Partial Area Under the ROC Curve (PAUC)
The Partial Area Under the ROC Curve (pAUC) is a metric for the performance of binary classifier. It is computed based on the receiver operating characteristic (ROC) curve that illustrates the diagnostic ability of a given binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.The area under the ROC curve (AUC) is often used to summarize in a single number the diagnostic ability of the classifier. The AUC is simply defined as the area of the ROC space that lies below the ROC curve. However, in the ROC space there are regions where the values of FPR or TPR are unacceptable or not viable in practice. For instance, the region where FPR is greater than 0.8 involves that more than 80% of negative subjects are incorrectly classified as positives: this is unacceptable in many real cases. As a consequence, the AUC computed in the entire ROC space (i.e., with both FPR and TPR ranging from 0 to 1) can provide misleading indications. To overcome this limitation of AUC, it was proposed to compute the area under the ROC curve in the area of the ROC space that corresponds to interesting (i.e., practically viable or acceptable) values of FPR and TPR.
  • 786
  • 21 Oct 2022
Topic Review
Optimal Interpolation for infrared satellite data
Thermal infrared remote sensing measurements are blinded to surface emissions under cloudiness because infrared sensors cannot penetrate thick cloud layers. Therefore, surface and atmospheric parameters can be retrieved only in clear sky conditions giving origin to spatial fields flagged with missing pieces of information. Motivated by this we present a methodology to retrieve missing values of some interesting geophysical variables retrieved from spatially scattered infrared satellite observations in order to yield level 3 (L3), regularly gridded, data. The technique is based on a 2-Dimensional (2D) Optimal Interpolation (OI) scheme. The goodness of the approach has been tested on 15-min temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and surface temperature (ST) products over South Italy (land and sea), on Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) concentration over North Italy and carbon monoxide (CO), sulfur dioxide (SO2) and NH3 concentrations over China. Sea surface temperature (SST) retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. For gases concentration, we have considered data from 3 different emission inventories, that is, Emissions Database for Global Atmospheric Research v3.4.2 (EDGARv3.4.2), the Regional Emission inventory in ASiav3.1 (REASv3.1) and MarcoPolov0.1, plus an independent study.
  • 694
  • 30 Oct 2020
Topic Review
Modelling Energy Transition in Germany
The expression energy transition indicates a long-term structural change in energy systems. It is not a new phenomenon: man has made several energy transitions in his short history on Earth. One of the most significant was shifting to an energy system based on fossil fuels (coal, oil, and natural gas) from one essentially based on wood; however, since the 1980s, it has been realized that fossil fuels are at the root of climate change due to carbon emissions into the atmosphere.
  • 468
  • 20 Apr 2022
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