This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and non-cancer aspects of a patient's health.

Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.

Description for file "simulation-particle-data.csv".

First row is header information, as follows:

Column 1, label "R":
Realisation label of the DFN.

Column 2, label "seed":
Seed used for generating DFN realisation.

Column 3, label "sdx":
Parameter controlling correlation length, as follows:
0 indicates no correlation (smooth fractures).
0.1 indicates weak correlation.
1.0 indicates strong correlation.

Column 4, label "particle_nr":
Particle number.

Column 5, label "flux":
Volumetric flux of water at location of particle (m3/s).
Used for obtaining flux injection mode.

Column 6, label "tau":
Travel time of particle for entire trajectory (yr).

Column 7, label "beta":
Transport resistence value of particle for entire trajectory (yr/m).

Description for file "simulation-particle-statistics.csv".

First row is header information, as follows:

Column 1, label "R":
Realisation label of the DFN.

Column 2, label "ensemble":
TRUE if this row refers to ensemble data (all realisations)
FALSE if this row refers to single realisations data

Column 3, label "sdx":
Parameter controlling correlation length, as follows:
0 indicates no correlation (smooth fractures).
0.1 indicates weak correlation.
1.0 indicates strong correlation.

Column 4, label "variable":
Identifier for tau or beta parameter:
t = tau, units yr.
b = beta, units yr/m.

Column 5, label "injection_mode":
Injection mode label:
FI = flux injection.
RI = resident injection.

Column 6, label "min":
Minimum value.

Column 7, label "perc0.001":
0.001 percentile value.

Column 8, label "perc0.01":
0.01 percentile value.

Column 9, label "perc0.1":
0.1 percentile value.

Column 10, label "median":
Median value.

Statistical mirroring-based ordinalysis (SM-based ordinalysis) measures the proximity or deviation of an individual's composite set of ordinal assessment scores from the highest positive ordinal scale point [3]. Within the framework of Kabirian-based optinalysis [1] and statistical mirroring [2], Statistical mirroring-based ordinalysis is conceptualized as the isoreflectivity (isoreflective pairing) of the composite set of ordinal assessment scores of an individual to the highest positive ordinal scale point of an established ordinal assessment scale, under customized and optimized choice of parameters. This represents the underlying assumption of statistical mirroring-based ordinalysis.

The process of Statistical mirroring-based ordinalysis comprises three distinct phases: a) Adaptive customization and optimization phase [3]: This phase represents the core of the methodology. This involves the adaptive customization and optimization of parameters to suit the requirements for statistical mirroring estimation in the given task [3]. b) Statistical mirroring computation phase [2]: This involves applying the adopted statistical mirroring type based on the phase 1 adaption. c) Optinalytic model calculation phase [1]: This phase is focused on computing estimates (such as the Kabirian coefficient of proximity, the probability of proximity, and the deviation) based on Kabirian-based isomorphic optinalysis models.

References: [1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. https://doi.org/10.1016/j.mex.2023.102400 [2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682 [3] K.B. Abdullahi, Statistical mirroring-based ordinalysis: A sensitive, robust, efficient, and ordinality-preserving descriptive method for analyzing ordinal assessment data, MethodsX 14 (2024) 103427. https://doi.org/10.1016/j.mex.2025.103427

Dataset of 418 primary school teachers' preferences on ICT-based teaching characteristics, analyzed using Discrete Choice Models, specifically McFadden's conditional logit model. The data includes variables such as subject area, grade level, and interactivity of digital resources. Each multivariate response is represented by three successive rows.

A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data products described herein. A sweep through the combined dataset (the "database") was made to isolate duplicate observations, or observations for the same well and on the same day. If a discrete value was present, it was retained as authoritative for the day and in descending order of priority daily-mean, daily-maximum, and daily minimum. Therefore, only a single record for a well and day are present in the dataset. The duplicate search removed 876 records and 31 wells were involved; in total, this is about 0.3 percent of the database. References: Asquith, W.H., Seanor, R.C., 2019, infoGW2visGWDB—An R groundwater data-processing utility for manipulating, checking the veracity, and converting an "infoGW" object to the "GWmaster" object for the visGWDB software with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9MK0B6L. Painter, J.A., and Westerman, D.A., 2018. Mississippi Alluvial Plain extent, November 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F70R9NMJ. U.S. Geological Survey, 2020, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed April 2, 2020, at https://doi.org/10.5066/F7P55KJN.

The Power Discrete Foundry market plays a critical role in the semiconductor industry, serving as a vital hub for the manufacturing of discrete power devices such as diodes, transistors, and thyristors. These components are essential for efficiently managing and converting electrical power across various application

The discrete manufacturing market plays a pivotal role in producing distinct and tangible products, which range from automobiles and electronics to machinery and consumer goods. Unlike process manufacturing, where raw materials are transformed into bulk products, discrete manufacturing focuses on individual units th

The Discrete Power Transistor market is a vital segment within the broader semiconductor industry, playing a critical role in the functionality of various electronic devices. These components serve as essential building blocks in electronic circuits, providing the necessary amplification and switching capabilities i

Author affiliation: York University Unreviewed Non UBC Faculty

In applications such as clinical safety analysis, the data of the experiments usually consist of frequency counts. In the analysis of such data, researchers often face the problem of multiple testing based on discrete test statistics, aimed at controlling family-wise error rate (FWER). Most existing FWER controlling procedures are developed for continuous data, which are often conservative when analyzing discrete data. By using minimal attainable p-values, several FWER controlling procedures have been specifically developed for discrete data in the literature. In this article, by using known marginal distributions of true null p-values, three more powerful stepwise procedures are developed, which are modified versions of the conventional Bonferroni, Holm and Hochberg procedures, respectively. It is shown that the first two procedures strongly control the FWER under arbitrary dependence and are more powerful than the existing Tarone-type procedures, while the last one only ensures control of the FWER in special settings. Through extensive simulation studies, we provide numerical evidence of superior performance of the proposed procedures in terms of the FWER control and minimal power. A real clinical safety data are used to demonstrate applications of our proposed procedures. An R package “MHTdiscrete” and a web application are developed for implementing the proposed procedures.

There is a growing appreciation of the multiple social and nonsocial factors influencing the foraging behavior of social animals, but little understanding of how these factors depend on habitat characteristics or individual traits. This partly reflects the difficulties inherent in using conventional statistical techniques to analyze multi-factor, multi-context foraging decisions. Discrete choice models provide a way to do so, and we demonstrate this by using them to investigate patch preference in a wild population of social foragers (chacma baboons, Papio ursinus). Data were collected from 29 adults across two social groups encompassing 683 foraging decisions over a six-month period, and the results interpreted using an information theoretic approach. Baboon foraging decisions were influenced by multiple nonsocial and social factors, and were often contingent on the characteristics of the habitat or individual. Differences in decision-making between habitats were consistent with changes in interference competition costs but not changes in social foraging benefits. Individual differences in decision-making were suggestive of a trade-off between dominance rank and social capital. Our findings emphasize that taking a multi-factor, multi-context approach is important to fully understand animal decision-making. We also demonstrate how discrete choice models can be used to achieve this.

Statistics Canada has developed a Web Data Service that provides access to data and metadata that we release each business day. This is a good option for users who want to consume a discrete amount of data points updates to Statistics Canada data. To obtain information on how to use and consume our Web Data Service, please read the Web Data Service User Guide.

Imports: ICT: EC: Semi: Parts: Discrete Device Semiconductor Parts data was reported at 6,627,833.000 USD in Mar 2025. This records an increase from the previous number of 3,966,513.000 USD for Feb 2025. Imports: ICT: EC: Semi: Parts: Discrete Device Semiconductor Parts data is updated monthly, averaging 6,278,511.000 USD from Jan 1996 (Median) to Mar 2025, with 351 observations. The data reached an all-time high of 25,260,096.000 USD in Jul 2022 and a record low of 1,039,824.000 USD in Mar 2009. Imports: ICT: EC: Semi: Parts: Discrete Device Semiconductor Parts data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s South Korea – Table KR.JA065: Trade Statistics: Import: Information & Communication Technology Industry.

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Electroencephalogram (EEG) is used to monitor child's brain during coma by recording data on electrical neural activity of the brain. Signals are captured by multiple electrodes called channels located over the scalp. Statistical analyses of EEG data includes classification and prediction using arrays of EEG features, but few models for the underlying stochastic processes have been proposed. For this purpose, a new strictly stationary strong mixing diffusion model with marginal multimodal (three-peak) distribution (MixGGDiff) and exponentially decaying autocorrelation function for modeling of increments of EEG data was proposed. The increments were treated as discrete-time observations and a diffusion process where the stationary distribution is viewed as a mixture of three non-central generalized Gaussian distributions (MixGGD) was constructed.Probability density function of a mixed generalized Gaussian distribution (MixGGD) consists of three components and is described using a total of 12 parameters:\muk, location parameter of each of the components,sk, shape parameter of each of the components, \sigma2k, parameter related to the scale of each of the components andwk, weight of each of the components, where k, k={1,2,3} refers to theindex of the component of a MixGGD. The parameters of this distribution were estimated using the expectation-maximization algorithm, where the added shape parameter is estimated using the higher order statistics approach based on an analytical relationship between the shape parameter and kurtosis.To illustrate an application of the MixGGDiff to real data, analysis of EEG data collected in Uganda between 2008 and 2015 from 78 children within age-range of 18 months to 12 years who were in coma due to cerebral malaria was performed. EEG were recorded using the International 10–20 system with the sampling rate of 500 Hz and the average record duration of 30 min. EEG signal for every child was the result of a recording from 19 channels. MixGGD was fitted to each channel of every child's recording separately, hence for each channel a total of 12 parameter estimates were obtained. The data is presented in a matrix form (dimension 79*228) in a .csv format and consists of 79 rows where the first row is a header row which contains the names of the variables and the subsequent 78 rows represent parameter estimates of one instance (i.e. one child, without identifiers that could be related back to a specific child). There are a total of 228 columns (19 channels times 12 parameter estimates) where each column represents one parameter estimate of one component of MixGGD in the order of the channels, thus columns 1 to 12 refer to parameter estimates on the first channel, columns 13 to 24 refer to parameter estimates on the second channel and so on. Each variable name starts with "chi" where "ch" is an abbreviation of "channel" and i refers to the order of the channel from EEG recording. The rest of the characters in variable names refer to the parameter estimate names of the components of a MixGGD, thus for example "ch3sigmasq1" refers to the parameter estimate of \sigma2 of the first component of MixGGD obtained from EEG increments on the third channel. Parameter estimates contained in the .csv file are all real numbers within a range of -671.11 and 259326.96.Research results based upon these data are published at https://doi.org/10.1007/s00477-023-02524-y

Statistics Canada has developed a Web Data Service that provides access to data and metadata that we release each business day. This is a good option for users who want to consume a discrete amount of data points updates to Statistics Canada data. To obtain information on how to use and consume our Web Data Service, please read the Web Data Service User Guide.

China Semiconductor Discrete Device: YoY: Total Liability data was reported at 6.583 % in Oct 2015. This records a decrease from the previous number of 6.602 % for Sep 2015. China Semiconductor Discrete Device: YoY: Total Liability data is updated monthly, averaging 9.075 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 39.911 % in Sep 2011 and a record low of -2.957 % in Mar 2015. China Semiconductor Discrete Device: YoY: Total Liability data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

The Functional Discrete Graphics Card market has become an integral part of the technology landscape, powering an array of applications from gaming and professional design to artificial intelligence and high-performance computing. These specialized components offer enhanced graphical performance and superior process

In the first quarter of 2022, Nvidia held a ** percent shipment share within the global PC discrete graphics processing unit (dGPU) market, whilst AMD held a share of ** percent. Intel recorded a share of **** percent of the dGPU market in the first quarter of 2022.