搜索结果: 1-15 共查到“统计学 modelling”相关记录28条 . 查询时间(0.073 秒)
Direct Regression Modelling of High-order Moments in Big Data
Big data Higher-order moment U-statistics Estimating equation Divide-and-conquer
2016/1/26
Big data problems present great challenges to statisti-cal analyses, especially from the computational side. In this paper, we consider regression estimation of high-order mo-ments in big data problem...
Statistical modelling of summary values leads to accurate Approximate Bayesian Computations
Statistical modelling summary values leads accurate Approximate Bayesian Computations
2013/6/14
Approximate Bayesian Computations (ABC) are considered to be noisy. We show that ABC can be set up to estimate the mode of the true posterior density exactly, or alternatively provide unbiased estimat...
Modelling time and vintage variability in retail credit portfolios: the decomposition approach
Age-period-cohort default Exogeneous EMV model Forecasting Macroeco-nomic Statistical model Vintage
2013/6/14
In this paper, we consider the problem of modelling historical data on retail credit portfolio performance, with a view to forecasting future performance, and facilitating strategic decision making. W...
Criticisms of modelling packet traffic using long-range dependence (extended version)
Criticisms modelling packet traffic long-range dependence
2013/5/2
This paper criticises the notion that long-range dependence is an important contributor to the queuing behaviour of real Internet traffic. The idea is questioned in two different ways. Firstly, a clas...
A dependent partition-valued process for multitask clustering and time evolving network modelling
A dependent partition-valued process multitask clustering time evolving network modelling
2013/4/27
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach wo...
Machine Learning for Bioclimatic Modelling
Machine Learning Bioclimatic Modelling Geographic Range Artificial Neural Network Evolutionary Algorithm
2013/5/2
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range s...
Common Mistakes when Applying Computational Intelligence and Machine Learning to Stock Market modelling
Computational intelligence machine learning stock market equities automated stock tradin mistakes.
2012/9/17
For a number of reasons, computational intelligence and machine learning methods have been largely dismissed by the professional community. The reasons for this are numerous and ...
Modelling interactions in high-dimensional data with Backtracking
Backtracking interactions Lasso parallel computing path algorithm.
2012/9/17
We study the problem of high-dimensional regression when there may be interacting vari-ables. We introduce a new idea called Backtracking, that can be incorporated into many existing high-dimensional ...
Modelling fixation locations using spatial point processes
Modelling fixation locations spatial point processes
2012/9/19
Whenever eye movements are measured, a central part of the analysis has to do withwheresubjects fixate, andwhy they fixated where they fixated. To a first approximation, a set of fixations can be view...
A SARIMAX coupled modelling applied to individual load curves intraday forecasting
SARIMA(X) modelling Time series analysis Exogenous covariates Forecasting Seasonality Stationarity Individual load curve.
2012/9/18
A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARI...
Modelling outliers and structural breaks in dynamic linear models with a novel use of a heavy tailed prior for the variances: An alternative to the Inverted Gamma
Modelling outliers structural breaks Inverted Gamma
2011/7/19
In this paper we propose a new wider class of hypergeometric heavy tailed priors that are given as the convolution of a Student-t density for the location parameter and a Scaled Beta2 prior for the va...
Modelling time to event with observations made at arbitrary times
Modelling observations RAFT RPH
2011/3/21
We introduce new methods of analysing time to event data via extended versions of the proportional hazards and accelerated failure time (AFT) models. In many time to event studies, the time of first o...
Semi-parametric dynamic time series modelling with applications to detecting neural dynamics
Dynamic time series modeling change-point testing Bayesian statistics statistics for neural data
2010/11/8
This paper illustrates novel methods for nonstationary time se-ries modeling along with their applications to selected problems in neuroscience. These methods are semi-parametric in that inferences ar...
Happy places or happy people?A multi-level modelling approach to the analysis of happiness and well-being
well-being multi-level modelling random effects combining data
2010/4/30
This paper aims to enhance our understanding of substantive questions regarding self-reported
happiness and well-being through the specification and use of multi-level models. To date, there have bee...
A Dynamic Modelling Strategy for Bayesian Computer Model Emulation
Computer model emulation Dynamic linear model backward sampling Gaussian process Markov chain Monte Carlo
2009/9/24
Computer model evaluation studies build statistical models of deterministic simulation-based predictions of field data to then assess and criticize the computer model and suggest refinemen...