Kernel Poisson regression for mixed input variables
نویسندگان
چکیده
منابع مشابه
Negative binomial and mixed Poisson regression
A number of methods have been proposed for dealing with extra-Poisson variation when doing regression analysis of count data. This paper studies negative-binomial regression models and examines efficiency and robustness properties of inference procedures based on them. The methods are compared with quasilikelihood methods. RESUME Plusieurs mkthodes ont ktk propokes en vue de traiter le probltme...
متن کاملBias-corrected AIC for selecting variables in Poisson regression models
ABSTRACT In the present paper, we consider the variable selection problem in Poisson regression models. Akaike’s information criterion (AIC) is the most commonly applied criterion for selecting variables. However, the bias of the AIC cannot be ignored, especially in small samples. We herein propose a new bias-corrected version of the AIC that is constructed by stochastic expansion of the maximu...
متن کاملFactors Affecting Hospital Length of Stay Using Mixed Poisson Regression Models
Background and purpose: Modeling of Hospital Length of Stay (LOS) is of great importance in healthcare systems, but there is paucity of information on this issue in Iran. The aim of this study was to identify the optimal model among different mixed poisson distributions in modeling the LOS and effective factors. Materials and methods: In this cross-sectional study, we studied 1256 records, inc...
متن کاملFast metabolite identification with Input Output Kernel Regression
MOTIVATION An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a s...
متن کاملPoisson Variables
A Poisson process is one in which events are randomly distributed in time, space or some other variable with the number of events in any non-overlapping intervals statistically independent. For example, naturally occurring gamma rays detected in a scintillation detector are randomly distributed in time, or chocolate chips in a cookie dough are randomly distributed in volume. For simplicity, we ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korean Data and Information Science Society
سال: 2012
ISSN: 1598-9402
DOI: 10.7465/jkdi.2012.23.6.1231