scholarly journals Model for diameter distribution from assortments volumes: theoretical formulation and a case application with a sample of timber trade data for clear-cut sections

Silva Fennica ◽  
2019 ◽  
Vol 53 (1) ◽  
Author(s):  
Jouni Siipilehto ◽  
Miika Rajala
Keyword(s):  
Theoretical Model ◽  
Goodness Of Fit ◽  
Stand Structure ◽  
Data Set ◽  
Trade Data ◽  
Stem Size ◽  
Clear Cut ◽  
Timber Trade ◽  
Ks Test ◽  
Stand Characteristics

This study examined a theoretical model for stand structures from the volumes of pulpwood and saw logs of clear-cut stands. The average stem size was used to estimate the number of cut trees. The distribution was solved using nonlinear derivative-free optimization. The truncated 2-parameter Weibull distribution was used to describe the stand structure of the commercial stems. This method was first tested with harvester data collected from seven clear-cut stands in southern Finland. Validation included reliability in the stand characteristics and goodness-of-fit of the species-specific distributions. The distributions provided unbiased estimates for the saw log volume, while the bias in the estimated pulpwood volume was 2%. The standard stand characteristics from the Weibull distributions corresponded notably well with the harvester data. A Kolmogorov-Smirnov (KS) test rejected two distributions out of 21 cases, when the accurate input variables were available for the theoretical model. The results of the study suggest that the presented method is a relevant option for predicting the stand structure. In practice, the reliability of the presented method was dependent on the quality of the information available from the stand prior to cutting. With a timber trade data set, the solution for the distribution for a clear-cut section was found. The goodness-of-fit was dependent on the accuracy of the visually assessed timber trade variables. Especially the average stem size proved difficult to assess due to high number of understorey pulpwood stems. Due to overestimated average stem sizes, the solved number of harvested trees was underestimated. Less than 50% of the distributions predicted for clear-cut sections passed the KS test.

scholarly journals Anger, Anxiety and Corruption Perceptions: Evidence from France

2017 ◽  
Vol 65 (4) ◽  
pp. 893-911 ◽  
Author(s):  
Sarah Birch ◽  
Nicholas J Allen ◽  
Katja Sarmiento-Mirwaldt
Keyword(s):  
Theoretical Model ◽  
Recent Work ◽  
Representative Sample ◽  
Adult Population ◽  
Data Set ◽  
Clear Cut ◽  
Unique Data ◽  
Political Judgement ◽  
People’S Perceptions

This article assesses the roles of anxiety and anger in shaping people’s perceptions of politicians’ integrity. Drawing on recent work on the role of affect in shaping political judgement, the article develops a theoretical model of the anticipated role of anger and anxiety in structuring reactions to allegations of political misconduct. The model is tested on a unique data set that includes results of an experiment fielded as part of a survey carried out in January 2013 among a representative sample of the French adult population. The analysis finds that those in whom politically dubious actions generate anxiety are more sensitive to contextual details than other respondents, although the role of anger in modulating ethical judgements is less clear-cut, dampening attention to information about negatively assessed behaviour but enhancing attention to information about behaviour that is assessed more positively.

Statistical models to identify stand development stages by means of stand characteristics

2011 ◽  
Vol 41 (1) ◽  
pp. 111-123 ◽  
Author(s):  
Markus O. Huber
Keyword(s):  
Discriminant Analysis ◽  
Statistical Models ◽  
Stand Structure ◽  
Multinomial Logistic Regression ◽  
Training Data ◽  
Stand Development ◽  
Quadratic Discriminant Analysis ◽  
Data Set ◽  
Development Stages ◽  
Stand Characteristics

Stand development stages differ mainly in terms of stand structure, stand density, and mortality patterns. As the fulfilment of socio-economic forest functions often depends on stand structure and density, knowledge of the frequency and distribution of stand development stages is needed for optimal forest management. Development stages have been previously identified only qualitatively by experts in forest ecology, but this study developed and compared statistical models to identify development stages by means of stand characteristics. Data from the Austrian National Forest Inventory with 4761 observations of stand development stages were used as the training data set for quadratic discriminant analysis and multinomial logistic regression. The models differ only marginally in terms of the hit ratio and the overall kappa statistic (both determined by means of an independent test data set). The quadratic discriminant analysis has the advantage that the user can reduce or even avoid the influence of the group size on the group-specific model performance by using equal prior probabilities. Furthermore, the discriminant analysis showed the best model behaviour in terms of the explanatory variables and performed best in identifying the stages that were infrequent in the training data set.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

scholarly journals Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

2021 ◽  
Vol 503 (2) ◽  
pp. 2688-2705
Author(s):  
C Doux ◽  
E Baxter ◽  
P Lemos ◽  
C Chang ◽  
A Alarcon ◽  
...  
Keyword(s):  
Dark Energy ◽  
Internal Consistency ◽  
Goodness Of Fit ◽  
Small Scale ◽  
P Value ◽  
Posterior Predictive Distribution ◽  
Data Set ◽  
Dark Energy Survey ◽  
Data Vector ◽  
Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

scholarly journals Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task

2010 ◽  
Vol 2 (2) ◽  
pp. 38-51 ◽  
Author(s):  
Marc Halbrügge
Keyword(s):  
Goodness Of Fit ◽  
Model Development ◽  
Cognitive Model ◽  
Training Data ◽  
Sequence Matching ◽  
Data Set ◽  
Depth Analysis ◽  
Stocks And Flows ◽  
Matching Techniques

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) taskThis paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

scholarly journals The Structural Model of Indicators of Educational Leadership for Primary School Principals

2021 ◽  
Author(s):  
Phalad Tipsrirach ◽  
Witoon Thacha ◽  
Prayuth Chusorn
Keyword(s):  
Theoretical Model ◽  
Educational Leadership ◽  
Primary School ◽  
Empirical Data ◽  
Goodness Of Fit ◽  
Structural Model ◽  
School Principals ◽  
Factor Loading ◽  
Primary School Principals ◽  
Fit Index

This research aimed at creating a structural model of the indicators of Educational Leadership for Primary School Principals in Thailand, which is considered to be a theoretical model that has been used to test for coherence with the empirical data collected from a sample group of 580 participants, who were selected from 30,719 Primary School Principals from across the country. To create this theoretical structural model, a study of the suitability of the indicators was carried out so that it could be further used in the selection within the model, as well as in the model’s coherence test with the empirical data and in the investigation of the factor loading. The results of the research were as follows: Firstly, all indicators, which had been applied in the research were selected and were then placed into the theoretical structural model because the average and distribution coefficient values were as set in the criteria. Secondly, the theoretical model is coherent with the empirical data as the values of relative Chi-square, Root Mean Square Error of Approximation, Goodness-of-Fit Index, Adjusted Goodness-of-Fit Index, Comparative Fit Index, and Normed Fit Index were as set in the criteria. Finally, the factor loadings of the key elements, sub-elements, and the indicators were as set in the criteria. This showed that the theoretical model from this research can be beneficial for the research population with construct validity.

scholarly journals Models for Tree Taper Form: The Gompertz and Vasicek Diffusion Processes Framework

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 80 ◽  
Author(s):  
Martynas Narmontas ◽  
Petras Rupšys ◽  
Edmundas Petrauskas
Keyword(s):  
Goodness Of Fit ◽  
Diffusion Processes ◽  
Parameters Estimation ◽  
Data Set ◽  
Mixed Effect ◽  
Fit Statistics ◽  
Stem Taper ◽  
Longitudinal Measurements ◽  
High Level ◽  
Taper Models

In this work, we employ stochastic differential equations (SDEs) to model tree stem taper. SDE stem taper models have some theoretical advantages over the commonly employed regression-based stem taper modeling techniques, as SDE models have both simple analytic forms and a high level of accuracy. We perform fixed- and mixed-effect parameters estimation for the stem taper models by developing an approximated maximum likelihood procedure and using a data set of longitudinal measurements from 319 mountain pine trees. The symmetric Vasicek- and asymmetric Gompertz-type diffusion processes used adequately describe stem taper evolution. The proposed SDE stem taper models are compared to four regression stem taper equations and four volume equations. Overall, the best goodness-of-fit statistics are produced by the mixed-effect parameters SDEs stem taper models. All results are obtained in the Maple computer algebra system.

scholarly journals Government Size and Economic Growth: A Review of International Literature

SAGE Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 215824401987720 ◽  
Author(s):  
Sheilla Nyasha ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Causal Relationship ◽  
Granger Causality ◽  
Empirical Support ◽  
Time Frame ◽  
Government Size ◽  
International Literature ◽  
Data Set ◽  
Clear Cut ◽  
Study Methodology

In this article, we survey the existing literature on the causal relationship between government size and economic growth, highlighting the theoretical and empirical evidence from topical work. To our knowledge, this study may well be the first study of its kind to survey, in detail, the existing literature on the causal relationship between government size and economic growth—in all the countries, whether developing or developed. By and large, our study shows that direction of causality between these two variables has four possible outcomes, and that all the outcomes have found empirical support, based on variations in the country or region under study, methodology, proxies, data set used, and time frame considered. However, of the four, the most prominent is the second view, which validates unidirectional Granger-causality from economic growth to government size, followed by the bidirectional Granger-causality category. The study, therefore, concludes that the causal relationship between government size and economic growth is far from being clear-cut.

scholarly journals Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5280
Author(s):  
Irini Furxhi ◽  
Finbarr Murphy
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
Information Gain ◽  
Sampling Technique ◽  
Exposure Dose ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Tissue Specific

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

The Arithmetic of Navigation Position Errors

1982 ◽  
Vol 35 (1) ◽  
pp. 28-38
Author(s):  
J. B. Parker
Keyword(s):  
Theoretical Model ◽  
Probability Density ◽  
Goodness Of Fit ◽  
Theoretical Models ◽  
Double Exponential Distribution ◽  
Position Errors ◽  
Double Exponential ◽  
The Family ◽  
Exponential Power ◽  
Image Position

In a notable series of articles, Hsu advances theoretical models which are used to graduate 7582 observations of aircraft lateral deviations. The goodness of fit of these models, as judged by the χ2 test, is satisfactory. Hsu's main theoretical model is the Double Double Exponential distribution (DDE), a three parameter model whose probability density function is given byOther model types are also considered, such as the family of exponential power distributions whose probability density is cited by Hsu insection 9. This leads to a four-parameter model, and the fit is (not surprisingly) better even than that of the DDE.

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