Model Selection

2021 ◽  
pp. 149-164
Author(s):  
Timothy E. Essington
Keyword(s):  
Information Theory ◽  
Model Selection ◽  
Goodness Of Fit ◽  
Danaus Plexippus ◽  
Predictive Ability ◽  
Information Criterion ◽  
Monarch Butterfly ◽  
Evaluation Framework ◽  
Alternative Hypotheses ◽  
Types Of Information

The chapter “Model Selection” provides a brief overview of alternative ways of thinking about what is (are) the best model(s) and shows the core motivation behind the Akaike information criterion as a measure of the predictive ability of a model fitted via maximum likelihood. It then gives stepwise practical guidance for using information theory as a basis of model selection, including nested versus non-nested models, goodness of fit, and overdispersion. Advanced topics cover some of the philosophy of information theory, other types of information theory criteria, and other ways of evaluating the predictive ability of models. As an example, the chapter examines the case of the Western monarch butterfly (Danaus plexippus plexippus), which, over the past two decades, has experienced a 97% decline from its historical average abundance, declining by 86% from 2017 to 2018 alone. Undoubtedly, there is more than one cause—indeed, overwintering habitat loss and pesticide use are both believed to be important contributors to the decline. Adopting a hypothesis-evaluation framework makes it possible to consider multiple alternative hypotheses simultaneously and measure degrees of support for alternative hypotheses.

scholarly journals Model Selection in Peptide-receptor Radionuclide Therapy for an Accurate Determination of Time Integrated Activity Coefficients

2021 ◽  
Vol 2019 (1) ◽  
pp. 012079
Author(s):  
N Atikah ◽  
A Riana ◽  
A Dwi ◽  
Z Anwari ◽  
Misrawati ◽  
...  
Keyword(s):  
Model Selection ◽  
Activity Coefficients ◽  
Radionuclide Therapy ◽  
Goodness Of Fit ◽  
Accurate Determination ◽  
Peptide Receptor Radionuclide Therapy ◽  
Information Criterion ◽  
Selection Function ◽  
Pharmacokinetic Data ◽  
Peptide Receptor

Abstract Calculation of accurate time-integrated activity coefficients (TIACs) is desirable in nuclear medicine dosimetry. The accuracy of the calculated TIACs is highly dependent on the fit function. However, systematic studies of determining a good function for peptide-receptor radionuclide therapy (PRRT) in different patients have not been reported in the literature. The aim of this study was to individually determine the best function for the calculation of TIACs in tumor and kidneys using a model selection based on the goodness of fit criteria and Corrected Akaike Information Criterion (AICc). The data used in this study was pharmacokinetic data of 111In-DOTATATE in tumor and kidneys obtained from 4 PRRT patients. Eleven functions with various parameterizations were formulated to describe the biokinetic data of 111In-DOTATATE in tumor and kidneys. The model selection was performed by choosing the best function from the function with sufficient goodness of fit based on the smallest AICc. Based on the results of model selection, function A 1 -(λ 1+λphys )t was selected as the best function for all tumor and kidneys in patients with meningioma tumors. By using this function, the calculated of TIACs could be more accurate for future patients with meningioma tumor.

scholarly journals SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were collected from the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(1, 0.11, 2)(1, 0, 1) 12 : Akaike information criterion (AIC): -631.31; SARIMA(1, 0, 2)(1, 1, 1) 12 : AIC: -227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.058; SARIMA: RMSE: 0.090). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

scholarly journals Upgrading Model Selection Criteria with Goodness of Fit Tests for Practical Applications

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 447
Author(s):  
Riccardo Rossi ◽  
Andrea Murari ◽  
Pasquale Gaudio ◽  
Michela Gelfusa
Keyword(s):  
Model Selection ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Information Criterion ◽  
Original Form ◽  
Residual Distribution ◽  
Practical Applications ◽  
Goodness Of Fit Tests ◽  
Squared Error ◽  
The Mean

The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other indicators derived from them are widely used for model selection. In their original form, they contain the likelihood of the data given the models. Unfortunately, in many applications, it is practically impossible to calculate the likelihood, and, therefore, the criteria have been reformulated in terms of descriptive statistics of the residual distribution: the variance and the mean-squared error of the residuals. These alternative versions are strictly valid only in the presence of additive noise of Gaussian distribution, not a completely satisfactory assumption in many applications in science and engineering. Moreover, the variance and the mean-squared error are quite crude statistics of the residual distributions. More sophisticated statistical indicators, capable of better quantifying how close the residual distribution is to the noise, can be profitably used. In particular, specific goodness of fit tests have been included in the expressions of the traditional criteria and have proved to be very effective in improving their discriminating capability. These improved performances have been demonstrated with a systematic series of simulations using synthetic data for various classes of functions and different noise statistics.

scholarly journals SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)12: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

scholarly journals A population-based method to determine the time-integrated activity in molecular radiotherapy

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Deni Hardiansyah ◽  
Ade Riana ◽  
Peter Kletting ◽  
Nouran R. R. Zaid ◽  
Matthias Eiber ◽  
...  
Keyword(s):  
Model Selection ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Population Based ◽  
Castration Resistant Prostate Cancer ◽  
Planar Imaging ◽  
Exponential Functions ◽  
Akaike Weight ◽  
Molecular Radiotherapy ◽  
Function Selection

Abstract Background The calculation of time-integrated activities (TIAs) for tumours and organs is required for dosimetry in molecular radiotherapy. The accuracy of the calculated TIAs is highly dependent on the chosen fit function. Selection of an adequate function is therefore of high importance. However, model (i.e. function) selection works more accurately when more biokinetic data are available than are usually obtained in a single patient. In this retrospective analysis, we therefore developed a method for population-based model selection that can be used for the determination of individual time-integrated activities (TIAs). The method is demonstrated at an example of [177Lu]Lu-PSMA-I&T kidneys biokinetics. It is based on population fitting and is specifically advantageous for cases with a low number of available biokinetic data per patient. Methods Renal biokinetics of [177Lu]Lu-PSMA-I&T from thirteen patients with metastatic castration-resistant prostate cancer acquired by planar imaging were used. Twenty exponential functions were derived from various parameterizations of mono- and bi-exponential functions. The parameters of the functions were fitted (with different combinations of shared and individual parameters) to the biokinetic data of all patients. The goodness of fits were assumed as acceptable based on visual inspection of the fitted curves and coefficients of variation CVs < 50%. The Akaike weight (based on the corrected Akaike Information Criterion) was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. Results The function $$A_{1} { }\beta { }e^{{ - \left( {\lambda_{1} + \lambda_{{{\text{phys}}}} } \right)t}} + A_{1} { }\left( {1 - \beta } \right){ }e^{{ - \left( {\lambda_{{{\text{phys}}}} } \right)t}}$$ A 1 β e - λ 1 + λ phys t + A 1 1 - β e - λ phys t with shared parameter $$\beta$$ β was selected as the function most supported by the data with an Akaike weight of 97%. Parameters $$A_{1}$$ A 1 and $$\lambda_{1}$$ λ 1 were fitted individually for every patient while parameter $$\beta { }$$ β was fitted as a shared parameter in the population yielding a value of 0.9632 ± 0.0037. Conclusions The presented population-based model selection allows for a higher number of parameters of investigated fit functions which leads to better fits. It also reduces the uncertainty of the obtained Akaike weights and the selected best fit function based on them. The use of the population-determined shared parameter for future patients allows the fitting of more appropriate functions also for patients for whom only a low number of individual data are available.

scholarly journals SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods Data on notified HFRS cases in Weifang city, Shandong Province were supplied by the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention from January 1, 2005 to December 31, 2018. The SARFIMA model considering both the short-memory and long-memory were performed to fit and predict the HFRS series. Besides, we compared accuracy of fitting and prediction between SARFIMA and SARIMA which were used widely in infectious diseases. Results Both SARFIMA and SARIMA models show good fit of data. Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(2, 0.15, 2)(1, 0, 0) 12 : Akaike information criterion (AIC): -630.61; SARIMA(2, 0, 2)(1, 1, 0) 12 : AIC: -196.04) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.067; SARIMA: RMSE: 0.111). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help us to improve the forecast of HFRS incidence.

scholarly journals SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background: The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. Methods: Data on notified HFRS cases in Weifang city, Shandong Province were collected from the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.Results: Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA(1, 0, 2)(1, 1, 1)12: AIC: -227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).Conclusions: The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

scholarly journals Monarch butterfly and milkweed declines substantially predate the use of genetically modified crops

2019 ◽  
Vol 116 (8) ◽  
pp. 3006-3011 ◽  
Author(s):  
J. H. Boyle ◽  
H. J. Dalgleish ◽  
J. R. Puzey
Keyword(s):  
Genetically Modified ◽  
Danaus Plexippus ◽  
Genetically Modified Crops ◽  
Monarch Butterfly ◽  
The United States ◽  
Gm Crops ◽  
Herbicide Resistant ◽  
The Past ◽  
Scientific Attention ◽  
Herbicide Use

Monarch butterfly (Danaus plexippus) decline over the past 25 years has received considerable public and scientific attention, in large part because its decline, and that of its milkweed (Asclepias spp.) host plant, have been linked to genetically modified (GM) crops and associated herbicide use. Here, we use museum and herbaria specimens to extend our knowledge of the dynamics of both monarchs and milkweeds in the United States to more than a century, from 1900 to 2016. We show that both monarchs and milkweeds increased during the early 20th century and that recent declines are actually part of a much longer-term decline in both monarchs and milkweed beginning around 1950. Herbicide-resistant crops, therefore, are clearly not the only culprit and, likely, not even the primary culprit: Not only did monarch and milkweed declines begin decades before GM crops were introduced, but other variables, particularly a decline in the number of farms, predict common milkweed trends more strongly over the period studied here.

scholarly journals 101 Evaluation of Growth Curve Functions for predicted weight in small holder crossbred dairy cattle in Tanzania

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 79-80
Author(s):  
Chinyere Ekine ◽  
Raphael Mrode ◽  
Edwin Oyieng ◽  
Daniel Komwihangilo ◽  
Gilbert Msuta ◽  
...  
Keyword(s):  
Growth Curve ◽  
Model Comparison ◽  
Predictive Ability ◽  
Nonlinear Least Squares ◽  
Information Criterion ◽  
Dairy Farms ◽  
Exponential Model ◽  
Males And Females ◽  
Small Holder ◽  
Two Parameter

Abstract Modelling the growth curve of animals provides information on growth characteristics and is important for optimizing management in different livestock systems. This study evaluated the growth curves of crossbred calves from birth to 30 months of age in small holder dairy farms in Tanzania using a two parameter (exponential), four different three parameters (Logistic, von Bertalanffy, Brody, Gompertz), and three polynomial functions. Predicted weights based on heart girth measurements of 623 male and 846 female calves born between 2016 and 2019 used in this study were from the African Dairy Genetic Gains (ADGG) project in selected milk sheds in Tanzania, namely Tanga, Kilimanjaro, Arusha, Iringa, Njomba and Mbeya. Each function was fitted separately to weight measurement of males and females adjusted for the effect of ward and season of birth using the nonlinear least squares (nls) functions in R statistical software. The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, all three polynomial and four parameter functions performed better and did not differ enough from each other in both males and females compared to the two-parameter exponential model. Predicted weight varied among the models and differed between males and females. The highest estimated weight was observed in the Brody model for both males (278.09 kg) and females (264.10 kg). Lowest estimated weight was observed in the exponential model. Estimated growth rate varied among models. For males, it ranged from 0.04 kg-0.08 kg and for females, from 0.05 kg-0.09 kg in the Brody model and logistic model respectively. Predictive ability across all fitted curves was low, ranging from 25% to approximately 29%. This could be due to the huge range of breed compositions in the evaluated crossbred calves which characterizes small holder dairy farms in this system and different levels of farm management.

scholarly journals High Survivorship of First-Generation Monarch Butterfly Eggs to Third Instar Associated with a Diverse Arthropod Community

Insects ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 567
Author(s):  
Misty Stevenson ◽  
Kalynn L. Hudman ◽  
Alyx Scott ◽  
Kelsey Contreras ◽  
Jeffrey G. Kopachena
Keyword(s):  
Host Plants ◽  
First Generation ◽  
Danaus Plexippus ◽  
Monarch Butterfly ◽  
Conservation Strategies ◽  
North Texas ◽  
Ecological Processes ◽  
Area Of Occupancy ◽  
Predator Abundance ◽  
A Site

Based on surveys of winter roost sites, the eastern migratory population of the monarch butterfly (Danaus plexippus) in North America appears to have declined in the last 20 years and this has prompted the implementation of numerous conservation strategies. However, there is little information on the survivorship of first-generation monarchs in the core area of occupancy in Texas, Oklahoma, and Louisiana where overwinter population recovery begins. The purpose of this study was to determine the survivorship of first-generation eggs to third instars at a site in north Texas and to evaluate host plant arthropods for their effect on survivorship. Survivorship to third instar averaged 13.4% and varied from 11.7% to 15.6% over three years. The host plants harbored 77 arthropod taxa, including 27 predatory taxa. Despite their abundance, neither predator abundance nor predator richness predicted monarch survival. However, host plants upon which monarchs survived often harbored higher numbers of non-predatory arthropod taxa and more individuals of non-predatory taxa. These results suggest that ecological processes may have buffered the effects of predators and improved monarch survival in our study. The creation of diverse functional arthropod communities should be considered for effective monarch conservation, particularly in southern latitudes.

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