Zero-inflated Poisson regression, with an application to defects in manufacturing

By Diane Lambert. 1992

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, positive): “The ZIP model (Lambert, 1992) consists of a degenerate distribution at zero mixed with an untruncated Poisson distribution.”
Effective Bayesian Modeling of Groups of Related Count Time Series (2014, neutral): “In many real-world series, one observes an excess of zero values compared to the probability under the NB (e.g., Lambert, 1992): this can arise for structural reasons in the underlying process (e.g., out-of-stock items or supply chain disruptions in a retail store context, both of which would…”
A Behavioral Theory of R&D Expenditures and Innovations: Evidence from Shipbuilding (2003, methodology): “To check for underreporting, I estimated a zeroinflated model (Lambert, 1992) and a model with heterogeneity and endogenous underreporting related to firm size (Terza, 1998).”
ACADEMIC INCENTIVES, RESEARCH ORGANIZATION AND PATENTING AT A LARGE FRENCH UNIVERSITY (2007, methodology): “In the first sub-section, we present the zero inflated negative binomial (ZINB) model ( Lambert 1992; Cameron and Trivedi, 1998; Greene, 1994, 2003; Winkelmann, 2003) which we use to mimic the invention phenomenon.”
Score Test for Zero Inflated Generalized Poisson Regression Model (2005, methodology): “In an application in manufacturing, Lambert ( 1992 ) considered zero inflated Poisson regression models where she says, “One interpretation is that slight, unobserved changes in the environment cause the process to move randomly back and forth between perfect state in which the defects are extremely”
Self-Censorship on Facebook (2013, methodology): “Due to the skew in the distributions (see Figure 2), we elected to model censorship rates for posts and comments using a zero-inflated negative binomial regression, or ZINB (Lambert 1992; Mwalili, Lessaffre, & Declerck 2008).”
Two‐part regression models for longitudinal zero‐inflated count data (2010, methodology): “D. Lambert (1992).”
Examination of the Predictive Validity of Preschool Early Literacy Skills (2007, methodology): “Zero-inflated Poisson models allow for analysis of two processes: one with respect to the zero outcomes and one with respect to the nonzero outcomes (Lambert, 1992; Muthen & Muthen, 2005).”
Bayes linear spaces (2010, background): “The exponential family (12) may be called zero-inflated Poisson family (Lambert, 1992) because it can be written”
Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data (2021, methodology): “Lambert (1992) also observed this problem in fitting ZINB regression model to an observed data set.”
Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models (1994, methodology): “Major credit card vendors are reluctant to open additional accounts for individuals with many cards, since the expected profit from the account diminishes when it must compete 34This is the model estimated by Lambert (1992), though she did suggest the more general specification used here.”
A great wall of patents: What is behind China's recent patent explosion? (2009, methodology): “Wechoose tomodel the twoprocesses explicitlyandseparately by adopting the Zero Inflated Poisson (ZIP) model proposed by Lambert (1992).”
Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros (2008, methodology): “The zero-inflated family of models was developed by Mullahy 16 and extended by Lambert 9 and Greene 5 .”
Victimization History and Posttraumatic Stress: An Analysis of Psychopathy Variants in Male Juvenile Offenders (2012, methodology): “Group differences in PTSD symptom counts were examined using zero-inflated Poisson, as the distribution contained a large number of zero-values (Lambert, 1992).”
Bayesian Analysis of Zero-Inflated Distributions (2003, methodology): “282 Rodrigues This type of model has been used for count data with excess of zeros in many practical problems like manufacturing defects (Lambert, 1992), counting disease lesions on plants (Rideout et al., 1998) and sexual behavior (Heilbron, 1994).”
Marginal models for zero inflated clustered data (2004, methodology): “The original and most common example is the ZIP regression model, introduced by Lambert (1992). Let y 1⁄4 (y1, .”