The adaptivity of thresholding wavelet estimators in heteroscedastic nonparametric model with negatively super-additive dependent errors

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

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Is it possible to predict habitat use by spawning salmonids? A test using California golden trout (Oncorhynchus mykiss aguabonita)

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f99-081 ◽

1999 ◽

Vol 56 (9) ◽

pp. 1576-1584 ◽

Cited By ~ 28

Author(s):

Roland A Knapp ◽

Haiganoush K Preisler

Keyword(s):

Oncorhynchus Mykiss ◽

Water Depth ◽

Parametric Model ◽

Water Velocity ◽

Habitat Characteristics ◽

Nonparametric Model ◽

Habitat Features ◽

Substrate Size ◽

Depth Water ◽

Spawning Sites

It is widely believed that stream salmonids select spawning sites based on water depth, water velocity, and substrate size. Attempts to predict spawning locations using these habitat features have met with little success, however. In this study, we used nonparametric logistic regression to determine what habitat features were associated with the locations chosen by spawning California golden trout (Oncorhynchus mykiss aguabonita). From this nonparametric model, we developed a parametric model that incorporated the habitat features most strongly associated with spawning sites and used this model to calculate the probability of use by spawning golden trout for specific stream locations. The overall nonparametric model was highly significant and explained 62% of the variation in spawning location. Four of the eight habitat variables, substrate size, water depth, water velocity, and stream width, had highly significant effects and alone explained 59% of the variation in spawning location. The results of a cross-validation procedure indicated that the parametric model generally provided a good fit to the data. These results indicate that location-specific probabilities of use were predictable based on easily measured habitat characteristics and that nonparametric regression, an approach still rarely used in ecological studies, may have considerable utility in the development of fish-habitat models. Given the escalating pace at which fish habitats are being altered, such models are increasingly important in predicting the effects of these alterations on populations.

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Linking climate, gross primary productivity, and site index across forests of the western United States

Canadian Journal of Forest Research ◽

10.1139/x11-086 ◽

2011 ◽

Vol 41 (8) ◽

pp. 1710-1721 ◽

Cited By ~ 53

Author(s):

Aaron R. Weiskittel ◽

Nicholas L. Crookston ◽

Philip J. Radtke

Keyword(s):

Climate Change ◽

Growth Models ◽

Gross Primary Production ◽

Site Index ◽

Nonparametric Model ◽

Climate Change Scenarios ◽

Geographic Scale ◽

Potential Productivity ◽

The One ◽

The Relationship

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.

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Bayesian Nonparametric Model for the Validation of Peptide Identification in Shotgun Proteomics

Molecular & Cellular Proteomics ◽

10.1074/mcp.m700558-mcp200 ◽

2008 ◽

Vol 8 (3) ◽

pp. 547-557 ◽

Cited By ~ 24

Author(s):

Jiyang Zhang ◽

Jie Ma ◽

Lei Dou ◽

Songfeng Wu ◽

Xiaohong Qian ◽

...

Keyword(s):

Peptide Identification ◽

Shotgun Proteomics ◽

Nonparametric Model ◽

Bayesian Nonparametric

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A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims

North American Actuarial Journal ◽

10.1080/10920277.2016.1247720 ◽

2017 ◽

Vol 21 (2) ◽

pp. 228-241 ◽

Cited By ~ 19

Author(s):

Liang Hong ◽

Ryan Martin

Keyword(s):

Nonparametric Model ◽

Bayesian Nonparametric ◽

Insurance Claims

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Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

Journal of Risk and Financial Management ◽

10.3390/jrfm11030052 ◽

2018 ◽

Vol 11 (3) ◽

pp. 52 ◽

Cited By ~ 2

Author(s):

Mark Jensen ◽

John Maheu

Keyword(s):

Dirichlet Process ◽

Joint Distribution ◽

Nonparametric Model ◽

Bayesian Nonparametric ◽

Excess Returns ◽

Dirichlet Process Prior ◽

Stock Market Returns ◽

Conditional Mean ◽

Risk Return ◽

Volatility Feedback

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution.

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