没血色 发表于 2025-3-23 12:20:08
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Arsenic in Latin America: Part II,ns) with data, quantifying relations between economic entities, and predicting out of sample. Alternatively, they may be explorative tools that reduce the dimensionality of complex information and help to discover structures in data, thereby motivating the direction of future theoretical thinking.亲爱 发表于 2025-3-23 18:37:12
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The Police Investigation of Art Fraudreal variables (NLM), nonnegative variables (lognormal, exponential regression), and binary variables (Probit, Logit) have a long lasting tradition, statistical models for nonnegative integers, or counts, have only recently attracted the attention of econometricians. This stands in sharp contrast toIncrement 发表于 2025-3-24 09:34:47
Arsenic in Latin America: Part II,ns) with data, quantifying relations between economic entities, and predicting out of sample. Alternatively, they may be explorative tools that reduce the dimensionality of complex information and help to discover structures in data, thereby motivating the direction of future theoretical thinking.MOAN 发表于 2025-3-24 12:54:30
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tarted in the mid-eighties. After more than one decade of intensive research, the litera ture has reached a level of maturity that calls for a systematic and accessible exposition of the main results and methods. Such an exposition is the aim of the book. Count data models have found their way into施加 发表于 2025-3-24 20:37:01
Laura M. Bexfield,L. Niel Plummerobserved heterogeneity or as models for selectivity, where unobserved heterogeneity reflects a lack of observability of independent variables, whereas selectivity reflects limits in the observability of the dependent variable. Hybrid models exists as well. A third category of models addresses the issue of a dependent count process.sebaceous-gland 发表于 2025-3-24 23:23:50
Pratika Singh,Azmi Khan,Amrita Srivastavaa synthesis of a diverse body of literature that hitherto was available largely in pieces, as well as a contribution to the progress of the methodology, establishing several new results and introducing new models.