Model Estimates Research Articles

Applying and analyzing the Model Portfolio Theory (MPT) by using Markowitz Model and index Model

In this study, I apply the Markovitz Model(“MM”) and Index Model(“IM”). These Portfolio Optimization models estimate and optimize U.S. equity portfolios with some realistic additional constraints. I was given a recent 20 years of historical daily total return data for ten stocks, which belong in groups to three different sectors (according to Yahoo! Finance), one (S&P 500) equity index (a total of eleven risky assets), and a proxy for risk-free rate (1-month Fed Funds rate). I aggregate the daily data to the monthly observations, and based on those monthly observations, I calculate all proper optimization inputs for the full Markowitz Model (“MM”) alongside the Index Model (“IM”). We use the optimization inputs (“IM”) and (“MM”) to explore some additional constraints.

Analysis of Road 3 Asia (R3A)’s Impacts on Border Trade of Thailand

Trade opening has become an important asset for economic development in many countries and an important engine of economic globalization (Wang, Shan-Li, et al, 2020). With continuous foster on the regional economic cooperation, countries with close proximity further promote the mobility and development of infrastructures and policies. Nevertheless, testing a trade integration model of bilateral trade is not sufficiently well estimated with the Bayesian approach to provide practical evidence of trade integration. Moreover, in identifying the factors determining trade integration, testing using the Bayesian gravity equation is essential. After performing a series of simulation experiments, a relationship between bilateral trade volume and simulated trade determinants was predicted for the trade model. The results of the estimated coefficients on GDP in Thailand and GDP of Yunnan province, China are positively significant predictors of the trade growth. The distance between the countries has a negatively significant estimation that implies barriers in trade. The model predicts trade integration, especially towards the trade on route R3A. The Bayesian approach of the gravity model gives robust estimates for determining the impact factor for the bilateral trade, including the fact that the elasticities of total trade volume with respect to distance, population, and the exchange rate of Thailand are negative while the GDP per capita are positively significant. Further, economic size, GDP per capita, and exchange rate of the destination, and population and area of Yunnan province are positively predicted by the model. The estimated parameters are directly the elasticities, in which increases in GDP is consistent with the higher trade volumes. Further, evidence of the gravity equation is used for understanding trade potential, and after some integrations, the estimation is applied for the real trade. The measures of bilateral trade resistance or costs associated with the trade flow has influenced the expanding of the bilateral trade in the model in the GMS economies. Finally, trade integration can be implemented with evidence and estimates of the gravity model. The Bayesian experiment for the estimation of the impacts of the trade integration on route R3A predicts an increase of GDP, population, exchange rate, and GDP per capita as predominant predictors in the Bayesian gravity model. Thus, the results revealed that economic size, bilateral distance, and GDP per capita has affected the plausible trade agreements for trade integration on route R3A.

Inferring the tree regeneration niche from inventory data using a dynamic forest model

Abstract. The regeneration niche of trees is governed by many processes and factors that are challenging to determine. Besides a species's geographic distribution, which determines if seeds are available, a myriad of local processes in forest ecosystems (e.g., competition and pathogens) exert influences on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses that are often data deficient. The ForClim forest gap model solved this problem by linking species traits to regeneration properties. However, this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an inverse calibration approach where we estimate regeneration parameters directly from a large dataset of unmanaged European forests. The inverse calibration was done using Bayesian inference, estimating shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration (i.e., the maximum regeneration rate). We find that the parameters determining the species' light niche (i.e., light requirements) are similar for the trait-based and calibrated values for both model variants, but only a more complex model variant that included competition between recruits leads to plausible estimates of the drought niche. The trait-derived temperature niche did not match to the estimates from either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the estimates for the complex model being closer to the trait-based parameters. In both model variants, the calibration strongly changed the parameters that determine regeneration intensity compared to the default. We conclude that the regeneration niche of trees can be recovered from a large forestry dataset in terms of the stand-level parameters light availability and regeneration intensity, while abiotic drivers (temperature and drought) are more elusive. The higher performance (better fit to hold out) of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations. Future research should focus on even greater environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.

Using camera traps and N‐mixture models to estimate population abundance: Model selection really matters

Abstract Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size. We used images of moose (Alces americanus) from camera traps to parameterize N‐mixture models and tested the effect of ecological conditions, the spatial scale of measurement, and the criteria used to define independent detections on estimates of population abundance. We compared the model estimates to those generated empirically with aerial survey data, the standard method for many species of ungulate. We explored the sensitivity of estimates to model choice based on the common statistical criterion of parsimony. The two most parsimonious N‐mixture models (i.e. AICc) were considerably biased, producing implausibly large and considerably imprecise estimates of abundance. Most of the other models produced estimates of moose abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N‐mixture models was not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggested that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N‐mixture model. We recommend using a suite of models to generate predictions of abundance instead of the single top‐ranked model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps provided a broader set of insights into the behaviour of moose and the co‐occurrence of competitors and predators.

An Exploration of Causal Relationships Between Behavioural/Emotional Difficulties and Academic Achievement: A Path Model Approach

ABSTRACT As the debate on whether uni-directional and/or bi-directional relationships exists between behavioural/emotional difficulties of school-aged learners, this study employed the path modelling approach to explore if there was a causal direction between behavioural/emotional difficulties (i.e. attention, aggressiveness, depression) and academic achievement (i.e. Korean language arts, mathematics), or vice versa over time using two time points. The final sample contained 1,735 students who attended high schools during 2014–2015 school year. Path modelling estimates showed that (a) behavioural/emotional difficulties caused difficulties in mathematics in a year; (b) difficulties in both Korean language arts and mathematics caused aggressiveness and depression; and (c) there was a bidirectional relation between aggressiveness and depression and mathematics performance. Further, there was a gender difference in reacting to their experiences in behavioural/emotional difficulties and academic difficulties. Overall, findings from the current study provide a significant contribution to the existing literature on the causal relation between behavioural/emotional difficulties and academic achievement, as well as supporting the gender difference in terms of the direction of this relation. Finally, practical implications and future directions for research were discussed.

Pseudo datasets explain artificial neural networks

AbstractMachine learning enhances predictive ability in various research compared to conventional statistical approaches. However, the advantage of the regression model is that it can effortlessly interpret the effect of each predictor. Therefore, interpretable machine-learning models are desirable as the deep-learning technique advances. Although many studies have proposed ways to explain neural networks, this research suggests an intuitive and feasible algorithm to interpret any set of input features of artificial neural networks at the population-mean level changes. The new algorithm provides a novel concept of generating pseudo datasets and evaluating the impact due to changes in the input features. Our approach can accurately obtain the effect estimate from single to multiple input neurons and depict the association between the predictive and outcome variables. According to computer simulation studies, the explanatory effects of the predictors derived by the neural network as a particular case could approximate the general linear model estimates. Besides, we applied the new method to three real-life analyzes. The results demonstrated that the new algorithm could obtain similar effect estimates from the neural networks and regression models. Besides, it yields better predictive errors than the conventional regression models. Again, it is worth noting that the new pipeline is much less computationally intensive than the SHapley Additive exPlanations (SHAP), which could not simultaneously measure the impact due to two or more inputs while adjusting for other features.

Atmospheric oxygen as a tracer for fossil fuel carbon dioxide: a sensitivity study in the UK

Abstract. We investigate the use of atmospheric oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO2 (ffCO2) estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO2 using APO.

Cross-national examination of the Distress Tolerance Scale using Rasch methodology

AbstractThe 15-item Distress Tolerance Scale (DTS) is a widely used psychometric measure with evidence generally supporting its reliability and validity. However, limitations related to its ordinal response format and lack of cross-cultural comparisons have yet to be investigated using appropriate methods. The Partial Credit Rasch model was used to evaluate and enhance the psychometric properties of the DTS using responses from 2550 adult participants from the United States of America (USA), England, Canada, South Africa, Spain, and Argentina. The initial poor fit of the DTS to the Rasch model was improved by removing one item and combining locally dependent items into three testlets. These modifications resulted in the best fit of the 14-item DTS to the Rasch model for all the countries in our study, providing evidence of unidimensionality, high reliability and invariance across countries, meditation practice, and gender. Meeting the expectations of the Rasch model permitted the development of ordinal-to-interval conversion algorithms derived from person estimates of the Rasch model. Using the ordinal-to-interval conversion algorithms published in this article, ordinal DTS scores can be transformed into interval-level data, enhancing the precision of this scale for future research and clinical use across people from the six countries in this study and across the English and Spanish versions of the 14-item DTS.

Reconstruction of warm-season temperatures in central Europe during the past 60 000 years from lacustrine branched glycerol dialkyl glycerol tetraethers (brGDGTs)

Abstract. Millennial-scale climate variations during the last glacial period, such as Dansgaard–Oeschger (DO) cycles and Heinrich events, have been extensively studied using ice core and marine proxy records. However, there is a limited understanding of the magnitude of these temperature fluctuations in continental regions, and questions remain about the seasonal signal of these climate events. This study presents a 60 000-year-long temperature reconstruction based on branched glycerol dialkyl glycerol tetraethers (brGDGTs) extracted from lake sediments from the Eifel Volcanic Field, Germany. brGDGTs are bacterial membrane-spanning lipids that are known to have a strong relationship with temperature, making them suitable for temperature reconstructions. We test several temperature calibration models on modern samples taken from soils and multiple maar lakes. We find a negative bias in brGDGT-based temperature estimates associated with water depth and anoxic conditions that can be corrected for by accounting for a brGDGT isomer that is only produced in anoxic conditions. The corrected temperature reconstruction correlates with proxy and climate model estimates of temperature spanning the same time period, validating the calibration approach we selected. However, millennial-scale variability is significantly dampened in the brGDGT record, and in contrast to other Northern Hemisphere climate records, during several Heinrich stadials, temperatures actually increase. We demonstrate that these apparent discrepancies can be explained by the unique seasonal response of the brGDGT paleothermometer to temperatures of months above freezing (TMAF). Our data support the view that warm-season temperatures in Europe varied minimally during the last glacial period and that abrupt millennial-scale events were defined by colder, longer winters. Our continuous high-resolution temperature reconstruction provides important information about the magnitude of seasonal climate variability during the last glacial period that can be used to test climate models and inform studies of paleoecological change.

New insights into estimating the cementation exponent of the tight and deep carbonate pore systems via rigorous numerical strategies

AbstractOne of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

‘Dead birds flying': can north American rehabilitated raptors released into the wild mitigate anthropogenic mortality?

As the human footprint expands to meet societal energy needs, as do the impacts to wildlife. Raptors in particular are highly susceptible to anthropogenic caused mortality. Industry sectors are encouraged to offset these causes of mortality. Several options to mitigate these losses have been proposed, including raptor rehabilitation. However, its role as a conservation tool is untested. Currently no peer‐reviewed demographic analyses exist using post‐release data from rehabilitated raptors to evaluate its effectiveness at continental scales. Our objectives were to estimate annual survival of rehabilitated and wild raptors, and then use those estimates in demographic models to assess potential effects at individual and population levels. We hypothesized that rehabilitated raptors would survive similarly to their wild counterparts after an acclimation period, and that longer‐lived species (K‐selected) would benefit most from these releases. We used US Geological Survey Bird Banding Lab band‐recovery data (1974–2018) from 20 raptor species for modeling survival of rehabilitated individuals (n = 125 740) in comparison to wild birds (n = 1 913 352). Results from 17 species with adequate recovery data indicated that five species rehabilitated ≠ wild survival, two species had uncertain estimates, and 10 species rehabilitated ≈ wild survival by years two and three post‐release. We acquired admission (n = 69 707) and release (n = 25 740) data from 24 rehabilitation centers across the US (2012–2021). We integrated survival, fecundity and numbers of releases into demographic models. These models quantified the extent to which rehabilitated raptors may contribute to broader conservation efforts, especially in the context of individual take. All but two species, had measurable numbers of individuals added to the population regardless of the number of releases. The general pattern was for K‐selected species to yield larger benefits from rehabilitated supplementation to the population. These results provide evidence that rehabilitation may serve as mitigation tool to offset incidental take.

Incorporating uncertainty within dynamic interoceptive learning.

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty. Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures. Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty. These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

Vulnerability and resilience in the face of climate changes in Senegal's drylands: measurement at the household level and determinant assessment

The resilience capacity of smallholder households is one of the main drivers of their ability to continue to farm and make investments in the fragile dryland regions. This paper aims to assess the resilience profile of smallholder farmers in the face of climate change and the factors influencing it in three dryland sub-regions of Senegal, namely, Louga, Kaffrine, and Thies. We developed a composite index of climate resilience (CICR) using data on farmers' perceptions of climate variability and their perceived ability to withstand, adapt, and bounce back in the event of climatic shocks. Drought, strong winds, and soil fertility decline because of climate change emerged as the main climate hazards impacting smallholder farming systems. The CICR value ranged from −2 for the most vulnerable households to +2 for the most resilient households. On average, all the households were found to be vulnerable, with an average CICR value of −0.2. The LOUGA region was the most vulnerable, with an average CICR value of −0.36, followed by THIES (-0.2). The KAFFRINE region was relatively less vulnerable, with a CICR value of −0.1. Ordered logit model estimates show that the chances of improving CICR decrease with the increase of the household head's age until 59 years. Access to training on climate-smart agricultural (CSA) practices and climate information appeared to have the potential to increase by 171% the chance of the household improving its resilience status. Analysis also shows that one more woman working off-farm or in-home gardening has the potential to multiply by four times the chances of households being more resilient. This highlights the importance of empowering women to enhance household resilience to climate change. The off-farm revenue increased the chance to improve the resilience status of the farm household by 62% and the receipt of transfer revenue by 50%. This study provides a robust method for quantifying resilience or wellbeing and its drivers and enriches our understanding of the resilience ability of farmers to climate change in a West African context. It can be useful in designing effective adaptation interventions and improving the overall wellbeing of smallholder farmers.

Population pharmacokinetics and dosing optimization of perampanel in children with epilepsy: A real-world study.

The purposes of this study were to explore the pharmacokinetics of perampanel (PER) in children with epilepsy, identify factors that contribute to pharmacokinetic variations among subjects, evaluate the connection between PER exposure and clinical outcome, and establish an evidence-based approach for tailoring individualized antiepileptic treatment in this specific population. In this prospective study, PER plasma concentrations and genetic information on metabolic enzymes were obtained from 194 patients younger than 18 years. The disposition kinetics of PER in pediatric patients following oral dosing were characterized using nonlinear mixed effect models. The effective range for the plasma concentration of PER was determined by assessing the efficacy and safety of PER treatment and analyzing the relationship between drug exposure and clinical response. Monte Carlo simulations were then performed to evaluate and optimize the current dosing regimens. The pharmacokinetic profile of PER was adequately described by a one-compartment model with first-order absorption and elimination. Body weight, total bilirubin level, and concomitant oxcarbazepine were found to have significant influences on PER pharmacokinetics. Model estimates of apparent clearance and volume of distribution were .016 ± .009 L/h/kg and 1.47 ± .78 L/kg, respectively. The effective range predicted from plasma concentration data in responders was 215-862 μg/L. Dosing scenarios stratified according to essential covariates were proposed through simulation analysis. In this study, we captured the pharmacokinetic/pharmacodynamic characteristics of PER in pediatric epilepsy patients through analysis of real-world data and adopted a pharmacometric approach to support an individualized dosing strategy for PER in this specific population.

4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections

Background and ObjectiveIn radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series. MethodsWe propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation. ResultsThe model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo. ConclusionsUnlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series.

Practical System Identification and Incremental Control Design for a Subscale Fixed-Wing Aircraft

An incremental differential proportional integral (iDPI) control law using eigenstructure assignment gain design is tested in flight on a subscale platform to validate its suitability for fixed-wing flight control. A kinematic relation for the aerodynamic side-slip angle rate is developed to apply a pseudo full state feedback. In order to perform the gain design and assessment, a plant model is estimated using flight test data from gyro, accelerometer, airspeed and surface deflection measurements during sine-sweep excitations. Transfer function models for the actuators and surface deflections are identified both in-flight and on the ground for several different actuators and control surfaces using hall sensor surface deflection measurements. The analysis reveals a large variation in bandwidth between the different types of servo motors. Flight test results are presented which demonstrates that the plant model estimates based on tests with good frequency excitation, high bandwidth actuators and surface deflection measurements can be used to reasonably predict the closed-loop dynamic behavior of the aircraft. The closed-loop flight test results of the iDPi control law show good performance and lays the groundwork for further development.

GRADE concept paper 8: judging the certainty of discrimination performance estimates of prognostic models in a body of validation studies

BackgroundPrognostic models incorporate multiple prognostic factors to estimate the likelihood of future events for individual patients based on their prognostic factor values. Evaluating these models crucially involves conducting studies to assess their predictive performance, like discrimination. Systematic reviews and meta-analyses of these validation studies play an essential role in selecting models for clinical practice. MethodsIn this paper, we outline 3 thresholds to determine the target for certainty rating in the discrimination of prognostic models, as observed across a body of validation studies. Results and ConclusionWe propose 3 thresholds when rating the certainty of evidence about a prognostic model’s discrimination. The first threshold amounts to rating certainty in the model’s ability to classify better than random chance. The other 2 approaches involve setting thresholds informed by other mechanisms for classification: clinician intuition or an alternative prognostic model developed for the same disease area and outcome. The choice of threshold will vary based on the context. Instead of relying on arbitrary discrimination cut-offs, our approach positions the observed discrimination within an informed spectrum, potentially aiding decisions about a prognostic model's practical utility.

Optimal Parameter Estimation Techniques for Complex Nonlinear Systems

AbstractAccurate parameter estimation and state identification within nonlinear systems are fundamental challenges addressed by optimization techniques. This paper fills a critical gap in previous research by investigating tailored optimization methods for parameter estimation in nonlinear system modeling, with a particular emphasis on chaotic dynamical systems. We introduce and compare three optimization methods: a gradient-based iterative algorithm, the Levenberg-Marquardt algorithm, and the Nelder-Mead simplex method. These methods are strategically employed to simplify complex nonlinear optimization problems, rendering them more manageable. Through a comprehensive exploration of the performance of these methods in determining parameters across diverse systems, including the van der Pol oscillator, the Rössler system, and pharmacokinetic modeling, our study revealed that the accuracy and reliability of the Nelder-Mead simplex method were consistent. The Nelder-Mead simplex algorithm emerged as a powerful tool, that consistently outperforms alternative methods in terms of root mean squared error (RMSE) and convergence reliability. Visualizations of trajectory comparisons and parameter convergence under various noise levels further emphasize the algorithm’s robustness. These studies suggest that the Nelder-Mead simplex method has potential as a valuable tool for parameter estimation in chaotic dynamical systems. Our study’s implications extend beyond theoretical considerations, offering promising insights for parameter estimation techniques in diverse scientific fields reliant on nonlinear system modeling.

Modeling Sales of Multigeneration Technology Products in the Presence of Frequent Repeat Purchases: A Fractional Calculus-Based Approach

Frequently releasing a new product generation has become a common practice to sustain sales over time, thus accurately forecasting the sales trajectory of each product generation plays a vital role in the short-, medium-, and long-term planning of a firm. Classic multigeneration diffusion models do not incorporate within-generation repeat purchases, making them unusable for product lines with high rates of such purchases. Concentrating on technology products, we develop a multigeneration sales model to fill this void. We demonstrate that the new model can be used for predictive and prescriptive analytics. Our empirical results show that the new model estimates and forecasts sales more accurately than a state-of-the-art benchmark model that does not account for within-generation repeat purchases, underscoring the importance of incorporating repeat purchases. Furthermore, we use two different versions of our model to examine market entry timing under two main strategies, that is, (i) a phase-out transition strategy in which firms continue to sell the old generation after the release of the new generation, and (ii) a total transition strategy in which firms discontinue the old generation after the introduction of the new generation. Our results indicate that the repeat purchases rate determines whether it is optimal to expedite or delay the new product launch, underscoring the importance of incorporating repeat purchases in market entry strategies.

Barriers to the adoption of multiple agricultural innovations: insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan

Barriers to the adoption of multiple agricultural innovations: insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan