scholarly journals A Comparison of Generalized Hyperbolic Distribution Models for Equity Returns

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Virginie Konlack Socgnia ◽  
Diane Wilcox
Keyword(s):  
Stock Returns ◽  
Goodness Of Fit ◽  
Temporal Stability ◽  
Information Criterion ◽  
Estimation Algorithm ◽  
Dependence Structure ◽  
Model Parameters ◽  
Distribution Models ◽  
Generalized Hyperbolic Distribution ◽  
Fit Statistics

We discuss the calibration of the univariate and multivariate generalized hyperbolic distributions, as well as their hyperbolic, variance gamma, normal inverse Gaussian, and skew Student’st-distribution subclasses for the daily log-returns of seven of the most liquid mining stocks listed on the Johannesburg Stocks Exchange. To estimate the model parameters from historic distributions, we use an expectation maximization based algorithm for the univariate case and a multicycle expectation conditional maximization estimation algorithm for the multivariate case. We assess the goodness of fit statistics using the log-likelihood, the Akaike information criterion, and the Kolmogorov-Smirnov distance. Finally, we inspect the temporal stability of parameters and note implications as criteria for distinguishing between models. To better understand the dependence structure of the stocks, we fit the MGHD and subclasses to both the stock returns and the two leading principal components derived from the price data. While the MGHD could fit both data subsets, we observed that the multivariate normality of the stock return residuals, computed by removing shared components, suggests that the departure from normality can be explained by the structure in the common factors.

scholarly journals Carbon stocks and fluxes in reforestated sites of Durango, Mexico

2016 ◽  
Vol 11 (2) ◽  
pp. 15-34 ◽  
Author(s):  
José de Jesús Návar Cháidez ◽  
Nicolás González ◽  
José Graciano
Keyword(s):  
Carbon Sequestration ◽  
Goodness Of Fit ◽  
Carbon Stocks ◽  
Small Scale ◽  
Environmental Services ◽  
Model Parameters ◽  
Class Model ◽  
Fit Statistics ◽  
Individual Trees ◽  
Two Hybrid

In this research, we present predictions of carbon sequestration by pines growing in reforested sites of Durango, Mexico. Four methodologies to predict carbon stocks in standing aboveground biomass were tested. Two models at the whole stand scale and two hybrid models between whole stand, stand class, and individual trees were fitted. A chronosequence of 23 small-scale reforested sites and stem analysis conducted on 60 trees were used to fit model parameters and estimate goodness of fit statistics. A stand class model produced a better fit to measure carbon stocks in aboveground standing biomass. 3 Reforested sites with native pine species are sequestering carbon at differential rates partially explained by density, species, micro site, climate and age of pines. Society is benefiting from the environmental services provided by carbon sequestration and conservation of native coniferous forests.

scholarly journals Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study

2020 ◽  
Vol 41 (1) ◽  
pp. 37-50
Author(s):  
Daniel Gallacher ◽  
Peter Kimani ◽  
Nigel Stallard
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Health Technology ◽  
Survival Models ◽  
Fit Statistics ◽  
Long Term Treatment ◽  
Parametric Survival

Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods’ suitability for selecting a parametric model through simulation of data replicating the follow-up of intervention arms for various time-to-event outcomes from 4 clinical trials. Follow-up was replicated through the consideration of recruitment duration and minimum and maximum follow-up times. Ten thousand simulations of each scenario were performed. We demonstrate that the different methods can result in disagreement over the best model and that it is inappropriate to base model selection solely on goodness-of-fit statistics without consideration of hazard behavior and plausibility of extrapolations. We show that typical trial follow-up can be unsuitable for extrapolation, resulting in unreliable estimation of multiple parameter models, and infer that selecting survival models based only on goodness-of-fit statistics is unsuitable due to the high level of uncertainty in a cost-effectiveness analysis. This article demonstrates the potential problems of overreliance on goodness-of-fit statistics when selecting a model for extrapolation. When follow-up is more mature, BIC appears superior to the other selection methods, selecting models with the most accurate and least biased estimates of RMST.

Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation

2000 ◽  
Vol 8 (4) ◽  
pp. 307-332 ◽  
Author(s):  
Simon Jackman
Keyword(s):  
Missing Data ◽  
Social Science ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Data Sampling ◽  
Fit Statistics ◽  
Bill Cosponsorship ◽  
Bayesian Simulation

Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary” quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item—response models for the measurement of respondent's levels of political information in public opinion surveys, the estimation and analysis of legislators' ideal points from roll-call data, and outlier-resistant regression estimates of incumbency advantage in U.S. Congressional elections

Do Cortical Neurons Process Luminance or Contrast to Encode Surface Properties?

2006 ◽  
Vol 95 (4) ◽  
pp. 2638-2649 ◽  
Author(s):  
Tony Vladusich ◽  
Marcel P. Lucassen ◽  
Frans W. Cornelissen
Keyword(s):  
Surface Properties ◽  
Cortical Neurons ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Statistical Technique ◽  
Model Parameters ◽  
Sample Population ◽  
Individual Response ◽  
Neural Basis ◽  
The Difference

On the one hand, contrast signals provide information about surface properties, such as reflectance, and patchy illumination conditions, such as shadows. On the other hand, processing of luminance signals may provide information about global light levels, such as the difference between sunny and cloudy days. We devised models of contrast and luminance processing, using principles of logarithmic signal coding and half-wave rectification. We fit each model to individual response profiles obtained from 67 surface-responsive macaque V1 neurons in a center-surround paradigm similar to those used in human psychophysical studies. The most general forms of the luminance and contrast models explained, on average, 73 and 87% of the response variance over the sample population, respectively. We used a statistical technique, known as Akaike's information criterion, to quantify goodness of fit relative to number of model parameters, giving the relative probability of each model being correct. Luminance models, having fewer parameters than contrast models, performed substantially better in the vast majority of neurons, whereas contrast models performed similarly well in only a small minority of neurons. These results suggest that the processing of local and mean scene luminance predominates over contrast integration in surface-responsive neurons of the primary visual cortex. The sluggish dynamics of luminance-related cortical activity may provide a neural basis for the recent psychophysical demonstration that luminance information dominates brightness perception at low temporal frequencies.

Instrumental and Ideological Union Commitment: Longitudinal Assessment of Construct Validity

2001 ◽  
Vol 17 (2) ◽  
pp. 98-111 ◽  
Author(s):  
Anders Sjöberg ◽  
Magnus Sverke
Keyword(s):  
Construct Validity ◽  
Temporal Stability ◽  
Strong Support ◽  
Internal Consistency Reliability ◽  
Measurement Model ◽  
Two Dimensions ◽  
Model Parameters ◽  
Longitudinal Assessment ◽  
Union Commitment ◽  
Union Participation

Summary: Previous research has identified instrumentality and ideology as important aspects of member attachment to labor unions. The present study evaluated the construct validity of a scale designed to reflect the two dimensions of instrumental and ideological union commitment using a sample of 1170 Swedish blue-collar union members. Longitudinal data were used to test seven propositions referring to the dimensionality, internal consistency reliability, and temporal stability of the scale as well as postulated group differences in union participation to which the scale should be sensitive. Support for the hypothesized factor structure of the scale and for adequate reliabilities of the dimensions was obtained and was also replicated 18 months later. Tests for equality of measurement model parameters and test-retest correlations indicated support for the temporal stability of the scale. In addition, the results were consistent with most of the predicted differences between groups characterized by different patterns of change/stability in union participation status. The study provides strong support for the construct validity of the scale and indicates that it can be used in future theory testing on instrumental and ideological union commitment.

The Temporal Stability of the NBD-Dirichlet Model Parameters

2011 ◽  
Author(s):  
Malcolm Wright ◽  
Lara Stocchi ◽  
Carl Driesener
Keyword(s):  
Temporal Stability ◽  
Model Parameters ◽  
Dirichlet Model

scholarly journals Dependence Modeling in Energy Markets using Sibuya-type Copulas

2017 ◽  
Vol 6 (3) ◽  
pp. 43
Author(s):  
Nikolai Kolev ◽  
Jayme Pinto
Keyword(s):  
Goodness Of Fit ◽  
Basic Assumption ◽  
Energy Markets ◽  
Dependence Structure ◽  
Dependence Function ◽  
Dependence Modeling ◽  
Goodness Of Fit Tests ◽  
Oil And Natural Gas ◽  
Error Terms ◽  
Time Invariant

The dependence structure between 756 prices for futures on crude oil and natural gas traded on NYMEX is analyzed  using  a combination of novel time-series and copula tools.  We model the log-returns on each commodity individually by Generalized Autoregressive Score models and account for dependence between them by fitting various copulas to corresponding  error terms. Our basic assumption is that the dependence structure may vary over time, but the ratio between the joint distribution of error terms and the product of marginal distributions (e.g., Sibuya's dependence function) remains the same, being time-invariant.  By performing conventional goodness-of-fit tests, we select the best copula, being member of the currently  introduced class of  Sibuya-type copulas.

scholarly journals Using Ultrasound and Inflammation to Improve Prediction of Ischemic Stroke: A Secondary Analysis of the Multi-Ethnic Study of Atherosclerosis

2021 ◽  
pp. 37-43
Author(s):  
Hediyeh Baradaran ◽  
Alen Delic ◽  
Ka-Ho Wong ◽  
Nazanin Sheibani ◽  
Matthew Alexander ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Goodness Of Fit ◽  
Primary Outcome ◽  
Stroke Risk ◽  
Secondary Analysis ◽  
Predictor Variables ◽  
Baseline Model ◽  
Fit Statistics ◽  
Ischemic Stroke Risk

Introduction: Current ischemic stroke risk prediction is primarily based on clinical factors, rather than imaging or laboratory markers. We examined the relationship between baseline ultrasound and inflammation measurements and subsequent primary ischemic stroke risk. Methods: In this secondary analysis of the Multi-Ethnic Study of Atherosclerosis (MESA), the primary outcome is the incident ischemic stroke during follow-up. The predictor variables are 9 carotid ultrasound-derived measurements and 6 serum inflammation measurements from the baseline study visit. We fit Cox regression models to the outcome of ischemic stroke. The baseline model included patient age, hypertension, diabetes, total cholesterol, smoking, and systolic blood pressure. Goodness-of-fit statistics were assessed to compare the baseline model to a model with ultrasound and inflammation predictor variables that remained significant when added to the baseline model. Results: We included 5,918 participants. The primary outcome of ischemic stroke was seen in 105 patients with a mean follow-up time of 7.7 years. In the Cox models, we found that carotid distensibility (CD), carotid stenosis (CS), and serum interleukin-6 (IL-6) were associated with incident stroke. Adding tertiles of CD, IL-6, and categories of CS to a baseline model that included traditional clinical vascular risk factors resulted in a better model fit than traditional risk factors alone as indicated by goodness-of-fit statistics. Conclusions: In a multiethnic cohort of patients without cerebrovascular disease at baseline, we found that CD, CS, and IL-6 helped predict the occurrence of primary ischemic stroke. Future research could evaluate if these basic ultrasound and serum measurements have implications for primary prevention efforts or clinical trial inclusion criteria.

scholarly journals BDS B1I multipath channel statistical model comparison between static and dynamic scenarios in dense urban canyon environment

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Xin Chen ◽  
Di He ◽  
Ling Pei
Keyword(s):  
Model Comparison ◽  
Satellite Orbit ◽  
Estimation Algorithm ◽  
Intermediate Frequency ◽  
Satellite System ◽  
Multipath Channel ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Distribution Models ◽  
Channel Models

Abstract Global Navigation Satellite System (GNSS) multipath channel models are fundamental and critical for signal simulation and receiver performance evaluation. They also aid the designing of suitable multipath error mitigation algorithms when the properties of multipath channel are available. However, there is insufficient existing research on BeiDou Navigation Satellite System (BDS) signal multipath channel models. In this study, multipath channel statistical models are established on the basis of extensive datasets of the BDS B1I signal. A multipath parameter estimation algorithm is designed to extract information of multipath rays from the intermediate frequency data. The delay, power loss, Doppler fading frequency, and lifetime distribution models for static and dynamic vehicle platforms are established and compared, and the effects of the satellite orbit type and platform speed on the models are analyzed. The results reveal the detailed distribution and variation characteristics of the multipath parameters and are valuable for the development of accurate urban navigation systems.

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