scholarly journals Estimation of suspended sediment and dissolved solid load in a Mediterranean semiarid karst stream using log-linear models

2018 ◽  
Vol 50 (1) ◽  
pp. 43-59 ◽  
Author(s):  
Alberto Martínez-Salvador ◽  
Carmelo Conesa-García
Keyword(s):  
Sediment Transport ◽  
Water Level ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Linear Models ◽  
Base Flow ◽  
Solid Load ◽  
Log Linear ◽  
Leave One Out ◽  
Southeast Spain

Abstract Many models have been developed to predict the sediment transport in watercourses. This paper attempts to test the effectiveness of log-linear models (LLM) to estimate the suspended (S-LMM), dissolved (D-LLM), and total suspended (T-LLM) load into a Mediterranean semiarid karst stream (the Argos River basin, in southeast Spain). An assessment of the supposed validity of each model and a leave-one-out cross-validation were carried out to determine their degree of statistical robustness. The T-LLM model showed higher prediction accuracy (R2 = 0.98, RMSE = 0.15, and PE = ±5.4–6.6%) than the D-LLM model (R2 = 0.97, RMSE = 0.16, and PE = ±5.5–6.8%) or the D-LLM model (R2 = 0.77, RMSE = 0.71, and PE = ±101–493%). In addition, different model variants, according to two flow patterns (FP1 = base flow and FP2 = rising water level), were developed. The FP2-SLLM model provided a very good fit (R2 = 0.94, RMSE = 0.34, and PE = ±25.3–61.5%), substantially improving the results of the S-LLM model.

scholarly journals Estimating infectious diseases incidence: validity of capture–recapture analysis and truncated models for incomplete count data

2007 ◽  
Vol 136 (1) ◽  
pp. 14-22 ◽  
Author(s):  
N. A. H. VAN HEST ◽  
A. D. GRANT ◽  
F. SMIT ◽  
A. STORY ◽  
J. H. RICHARDUS
Keyword(s):  
Infectious Disease ◽  
Count Data ◽  
Disease Surveillance ◽  
Cross Validation ◽  
Linear Models ◽  
Disease Incidence ◽  
Infectious Disease Surveillance ◽  
Number Of Patients ◽  
Capture Recapture ◽  
Log Linear

SUMMARYCapture–recapture analysis has been used to evaluate infectious disease surveillance. Violation of the underlying assumptions can jeopardize the validity of the capture–recapture estimates and a tool is needed for cross-validation. We re-examined 19 datasets of log-linear model capture–recapture studies on infectious disease incidence using three truncated models for incomplete count data as alternative population estimators. The truncated models yield comparable estimates to independent log-linear capture–recapture models and to parsimonious log-linear models when the number of patients is limited, or the ratio between patients registered once and twice is between 0·5 and 1·5. Compared to saturated log-linear models the truncated models produce considerably lower and often more plausible estimates. We conclude that for estimating infectious disease incidence independent and parsimonious three-source log-linear capture–recapture models are preferable but truncated models can be used as a heuristic tool to identify possible failure in log-linear models, especially when saturated log-linear models are selected.

scholarly journals Comparison of LDA and SPRT on Clinical Dataset Classifications

2011 ◽  
Vol 4 ◽  
pp. BII.S6935 ◽  
Author(s):  
Chih Lee ◽  
Brittany Nkounkou ◽  
Chun-Hsi Huang
Keyword(s):  
Learning Community ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Error Rates ◽  
Close Relative ◽  
Classification Error ◽  
Class Label ◽  
Normality Assumption ◽  
Clinical Dataset ◽  
Leave One Out

In this work, we investigate the well-known classification algorithm LDA as well as its close relative SPRT. SPRT affords many theoretical advantages over LDA. It allows specification of desired classification error rates α and β and is expected to be faster in predicting the class label of a new instance. However, SPRT is not as widely used as LDA in the pattern recognition and machine learning community. For this reason, we investigate LDA, SPRT and a modified SPRT (MSPRT) empirically using clinical datasets from Parkinson's disease, colon cancer, and breast cancer. We assume the same normality assumption as LDA and propose variants of the two SPRT algorithms based on the order in which the components of an instance are sampled. Leave-one-out cross-validation is used to assess and compare the performance of the methods. The results indicate that two variants, SPRT-ordered and MSPRT-ordered, are superior to LDA in terms of prediction accuracy. Moreover, on average SPRT-ordered and MSPRT-ordered examine less components than LDA before arriving at a decision. These advantages imply that SPRT-ordered and MSPRT-ordered are the preferred algorithms over LDA when the normality assumption can be justified for a dataset.

scholarly journals Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy

2019 ◽  
Vol 60 (6) ◽  
pp. 818-824 ◽  
Author(s):  
Takuya Mizutani ◽  
Taiki Magome ◽  
Hiroshi Igaki ◽  
Akihiro Haga ◽  
Kanabu Nawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Malignant Glioma ◽  
Survival Time ◽  
Treatment Duration ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Learning Model ◽  
Machine Learning Model ◽  
Prescription Dose ◽  
Leave One Out

ABSTRACT The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose–volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike’s information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P < 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model.

Static and non-linguistic quantitative indicators to evaluate Japanese comic dialogues of Manzai

2018 ◽  
Vol 31 (1) ◽  
pp. 39-64
Author(s):  
Tetsuya Maeshiro
Keyword(s):  
Prediction Accuracy ◽  
Semantic Processing ◽  
Cross Validation ◽  
National Level ◽  
Rank Correlation ◽  
Time Sequence ◽  
Sequence Information ◽  
The Mean ◽  
Leave One Out ◽  
Quantitative Indicators

AbstractThis paper proposes the use of quantitative indicators to evaluate the comedic success of Japanese “Manzai” performances without using semantic processing or time sequence information. The validity of the proposed indicators was verified by predicting the rankings of the final rounds and decision matches of ten M1 Grand Prix, a national-level humor contest in Japan, using leave-one-out cross validation. The results demonstrate that the proposed indicators are able to predict the ranking of Manzai championships as the mean prediction precision was 0.58 (rank correlation) for final rounds, and 0.70 (champion prediction accuracy) for the decision matches.

Identifying human microRNA–disease associations by a new diffusion-based method

2015 ◽  
Vol 13 (04) ◽  
pp. 1550014 ◽  
Author(s):  
Bo Liao ◽  
Sumei Ding ◽  
Haowen Chen ◽  
Zejun Li ◽  
Lijun Cai
Keyword(s):  
Biomedical Research ◽  
Prediction Accuracy ◽  
Information Sources ◽  
Cross Validation ◽  
Disease Association ◽  
Global Network ◽  
Disease Similarity ◽  
Disease Associations ◽  
Network Similarity ◽  
Leave One Out

Identifying the microRNA–disease relationship is vital for investigating the pathogenesis of various diseases. However, experimental verification of disease-related microRNAs remains considerable challenge to many researchers, particularly for the fact that numerous new microRNAs are discovered every year. As such, development of computational methods for disease-related microRNA prediction has recently gained eminent attention. In this paper, first, we construct a miRNA functional network and a disease similarity network by integrating different information sources. Then, we further introduce a new diffusion-based method (NDBM) to explore global network similarity for miRNA–disease association inference. Even though known miRNA–disease associations in the database are rare, NDBM still achieves an area under the ROC curve (AUC) of 85.62% in the leave-one-out cross-validation in improving the prediction accuracy of previous methods significantly. Moreover, our method is applicable to diseases with no known related miRNAs as well as new miRNAs with unknown target diseases. Some associations who strongly predicted by our method are confirmed by public databases. These superior performances suggest that NDBM could be an effective and important tool for biomedical research.

On Cross Validation for Model Selection

1999 ◽  
Vol 11 (4) ◽  
pp. 863-870 ◽  
Author(s):  
Isabelle Rivals ◽  
Léon Personnaz
Keyword(s):  
Model Selection ◽  
Nonlinear Model ◽  
Cross Validation ◽  
Linear Models ◽  
Statistical Tests ◽  
Free Lunch ◽  
Leave One Out ◽  
No Free Lunch ◽  
Selection Of

In response to Zhu and Rower (1996), a recent communication (Goutte, 1997) established that leave-one-out cross validation is not subject to the “no-free-lunch” criticism. Despite this optimistic conclusion, we show here that cross validation has very poor performances for the selection of linear models as compared to classic statistical tests. We conclude that the statistical tests are preferable to cross validation for linear as well as for nonlinear model selection.

scholarly journals Predictive Modeling in Race Walking

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Krzysztof Wiktorowicz ◽  
Krzysztof Przednowek ◽  
Lesław Lassota ◽  
Tomasz Krzeszowski
Keyword(s):  
Prediction Error ◽  
Cross Validation ◽  
Linear Models ◽  
Lasso Regression ◽  
Training Process ◽  
Nonlinear Part ◽  
Race Walking ◽  
Multivariable Models ◽  
Using Data ◽  
Leave One Out

This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

scholarly journals Assessing bite force estimates in extinct mammals and archosaurs using phylogenetic predictions

2020 ◽  
Author(s):  
Manabu Sakamoto
Keyword(s):  
Body Size ◽  
Predictive Models ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Performance Measure ◽  
Bite Force ◽  
Training Set ◽  
Biomechanical Modelling ◽  
Phylogenetic Regression ◽  
Leave One Out

ABSTRACTBite force is an ecologically important biomechanical performance measure is informative in inferring the ecology of extinct taxa. However, biomechanical modelling to estimate bite force is associated with some level of uncertainty. Here, I assess the accuracy of bite force estimates in extinct taxa using a Bayesian phylogenetic prediction model. I first fitted a phylogenetic regression model on a training set comprising extant data. The model predicts bite force from body mass and skull width while accounting for differences owning to biting position. The posterior predictive model has a 93% prediction accuracy as evaluated through leave-one-out cross-validation. I then predicted bite force in 37 species of extinct mammals and archosaurs from the posterior distribution of predictive models.Biomechanically estimated bite forces fall within the posterior predictive distributions for all except four species of extinct taxa, and are thus as accurate as that predicted from body size and skull width, given the variation inherent in extant taxa and the amount of time available for variance to accrue. Biomechanical modelling remains a valuable means to estimate bite force in extinct taxa and should be reliably informative of functional performances and serve to provide insights into past ecologies.

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