• estimates
  • In general, the estimates of reliability from either the discrete model or the binary data analysis were close to estimates from the Weibull model for a given number of uncensored records in this simplified case of a balanced design. (pubfacts.com)
  • The objective of this study was, by means of simulation, to quantify the effect of ignoring individual heterogeneity in Weibull sire frailty models on parameter estimates and to address the consequences for genetic inferences. (pubfacts.com)
  • The objectives of the present study were to investigate through simulations a new variance estimator and to compare the estimates from the WC model and standard logistic regression for estimating the effects of correlated temporal aspects of exposure with detailed information on exposure history. (biomedcentral.com)
  • The superpopulation variance estimator provided better estimates than the robust sandwich variance estimator and the WC model provided accurate estimates of the effects of correlated aspects of temporal patterns of exposure. (biomedcentral.com)
  • In this situation the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. (wikipedia.org)
  • Other HR models have different formulations and the interpretation of the parameter estimates differs accordingly. (wikipedia.org)
  • The log-rank test statistic compares estimates of the hazard functions of the two groups at each observed event time. (wikipedia.org)
  • subset
  • a missing-data filter function, applied to the model.frame, after any subset argument has been used. (psu.edu)
  • Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn. (wikipedia.org)
  • Weibull
  • and 2) to examine the relationship between genetic parameters from a Weibull model, a discrete proportional hazard model, and a binary data analysis using a linear model. (pubfacts.com)
  • Data were simulated using the Weibull frailty model with two different shapes of the Weibull distribution. (pubfacts.com)
  • Three different statistical models were investigated in this study: a Weibull model, a discrete-time model (a proportional hazard model assuming that the survival data are measured on a discrete scale with few classes), and a linear model based upon binary data. (pubfacts.com)
  • An alternative derivation using basic expressions of reliabilities in sire models suggests a simple equation for the heritability on the original scale (effective heritability) that is not dependent on the Weibull parameters. (pubfacts.com)
  • Although selection response from the binary data analysis depends on the end of interval point, there is a relatively good agreement between selection responses in the Weibull model and the binary data analysis. (pubfacts.com)
  • Comparison between a Weibull proportional hazards model and a linear model for predicting the genetic merit of US Jersey sires for daughter longevity. (pubfacts.com)
  • Predicted transmitting abilities (PTA) of US Jersey sires for daughter longevity were calculated using a Weibull proportional hazards sire model and compared with predictions from a conventional linear animal model. (pubfacts.com)
  • The effect of ignoring individual heterogeneity in Weibull log-normal sire frailty models. (pubfacts.com)
  • Data were simulated according to balanced half-sib designs using Weibull log-normal animal frailty models with a normally distributed residual effect on the log-frailty scale. (pubfacts.com)
  • In another design - the Weibull proportional hazards design - the failure times are presumed to follow a theoretical circulation understood as the Weibull circulation. (statshelponline.com)
  • probability
  • Poisson regression models are generalized linear models with the logarithm as the (canonical) link function, and the Poisson distribution function as the assumed probability distribution of the response. (wikipedia.org)
  • In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam. (wikipedia.org)
  • In biostatistics, the researcher may be interested in trying to model the probability of a patient being diagnosed with a certain type of cancer based on knowing, say, the incidence of that cancer in his or her family. (wikipedia.org)
  • In business, the marketer may be interested in modelling the probability of an individual purchasing a product based on the price of that product. (wikipedia.org)
  • parameters
  • Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. (wikipedia.org)
  • Parametric models make "specific assumptions with regard to one or more of the population parameters that characterize the underlying distribution(s)", while non-parametric regressions make fewer assumptions than their parametric counterparts. (wikipedia.org)
  • approaches
  • The instantaneous hazard rate is the limit of the number of events per unit time divided by the number at risk, as the time interval approaches 0. (wikipedia.org)
  • Robust regression include a number of modelling approaches to handle high leverage observations or violation of assumptions. (wikipedia.org)
  • assess
  • The objectives of the proposed approach are: to provide a stress condition model for health monitoring, to assess the WT s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. (comillas.edu)
  • analytical
  • The WC model with the superpopulation variance estimator provides an alternative analytical approach for estimating the effects of time-varying exposures with detailed history exposure information in case-control studies, especially if many subjects have time-varying exposure intensity over lifetime, and if only one control is available for each case. (biomedcentral.com)
  • In most applications, involuntary reasons for churn are excluded from the analytical models. (wikipedia.org)
  • Deployment : Predictive model deployment provides the option to deploy the analytical results into everyday decision making process to get results, reports and output by automating the decisions based on the modelling. (wikipedia.org)
  • However, people are increasingly using the term to refer to related analytical disciplines, such as descriptive modeling and decision modeling or optimization. (wikipedia.org)
  • factors
  • Multiple linear regression models were used to identify anthropometric, clinical, behavioral, and dietary factors associated with fasting insulin and glucose in a subcohort of non-diabetics in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n=366). (pubmedcentralcanada.ca)
  • One of the main objectives of modeling customer churn is to determine the causal factors, so that the company can try to prevent the attrition from happening in the future. (wikipedia.org)
  • Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. (wikipedia.org)
  • function
  • This paper puts a focus on the hazard function of inter-trade durations to characterize the intraday trading process. (uni-konstanz.de)
  • We show, based on an exogenous information process, that the way traders aggregate information has implications for the shape of the hazard function. (uni-konstanz.de)
  • predictor
  • 1978). The case of multiple predictor variables subject to variability (possibly correlated) has been well-studied for linear regression, and for some non-linear regression models. (wikipedia.org)
  • Other non-linear models, such as proportional hazards models for survival analysis, have been considered only with a single predictor subject to variability. (wikipedia.org)
  • In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. (wikipedia.org)
  • That is, a multiple regression model with colinear predictors can indicate how well the entire bundle of predictors predicts the outcome variable, but it may not give valid results about any individual predictor, or about which predictors are redundant with respect to others. (wikipedia.org)
  • The model is "simple" in that each has only one independent, or predictor, variable, and it is "binary" in that the dependent variable can take on only one of two values: cancer or no cancer, and purchase or does not purchase. (wikipedia.org)
  • prediction
  • By deploying new technologies such churn prediction models coupled with effective retention programs, customer attrition could be better managed to stem the significant revenue loss from defecting customers. (wikipedia.org)
  • The log-rank test has been shown to be too permissive a test, allowing significant results for survivorship prediction models that have low accuracy. (wikipedia.org)
  • Nearly any regression model can be used for prediction purposes. (wikipedia.org)
  • data
  • This approach to survival data is called application of the Cox proportional hazards model, sometimes abbreviated to Cox model or to proportional hazards model. (wikipedia.org)
  • The results of the different models were finally compared for estimating the effects of correlated aspects of occupational exposure to asbestos on the risk of mesothelioma, using population-based case-control data. (biomedcentral.com)
  • From an inspection of conditional transaction probabilities based on Bund future transaction data of the DTB we find a decreasing hazard shape providing evidence for the use of non-trading intervals as an indication for the absence of price information among market participants. (uni-konstanz.de)
  • Applied longitudinal data analysis: Modeling change and event occurrence New York, USA: Oxford University Press. (pixnet.net)
  • Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. (wikipedia.org)
  • 1992
  • in a 1992 paper, Wei pointed out that the Buckley-James model has no theoretical justification and lacks robustness, and reviewed alternatives. (wikipedia.org)