Resulting Parameter Estimates Research Articles

Impedance Parameters Estimation of An RLCM Ladder Network Using Subspace and Similarity Transformation Approach

ABSTRACT Numerous physical systems such as transformers, sensors, and biomaterials are analysed using their equivalent ladder network model. Impedance parameters of a ladder network consist of combinations of resistances, inductances, and capacitances. Accurately determining any device impedance parameters directly from the measured time-domain input-output data is challenging. The use of non-linear optimization algorithms does not always work due to the problem being ill-conditioned. Also, the obtained parameters may not be physically realizable. In this work, the problem is approached from a black-box system identification point of view, followed by a physical parameter extraction step. The necessary current and voltage equations obtained from first principles are utilized to build a physical model in the state-space domain. With the use of a subspace system identification algorithm, the black-box parameter matrices are determined from input-output data, which do not correspond to the physical parameter matrices. However, both sets of parameters can be related through a similarity transformation matrix, leading to the extraction of the physical parameters. The novelty of the approach lies in solving the non-linear parameter estimation problem linearly without initialization. The presence of mutual inductances between various transformer winding sections makes its ladder network the most complex one. Therefore, in this paper, the ladder network model of a transformer winding is considered. As an example of the methodology, a six-section transformer ladder network is simulated and the parameter estimation results are used to validate the proposed algorithm. The same approach can be applied to estimate any other device impedance parameters.

Statistical measure researches for the impacts of COVID-19 on Shanghai Securities Composite Index: based on mode regression model using skew-normal distribution

The economy of China has been affected by the COVID-19 and it is necessary to measure and investigate it. The mode regression model and its data deletion model using skew-normal distribution are established via the industry sub-index data of Shanghai Stock Exchange. Statistical diagnosis method of the model is proposed by using Pena distance diagnosis statistics, and the unknown parameters are estimated by the EM algorithm facilitated by gradient descent (GDEM) method. The results show that the outliers are accurately identified by the proposed statistical diagnostic method, and parameter estimation results indicate that the contribution of all industries to economy during the COVID-19 is lower than before, among which industry and commerce are the hardest hit.

ESTIMATING OF PHOTOVOLTAIC CELLS/MODULES PARAMETERS USING A TRIANGULAR ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHM

Background: Photovoltaic (PV) systems have become a promising renewable energy technology for electricity sources. The PV parameter estimation plays a vital role in modeling PV systems. Even though many optimization algorithms have been presented to obtain PV parameters, it is still challenging to investigate high-performance algorithms. Aim: This study aimed to propose a triangular adaptive differential evolution (TADE) algorithm to give a precise estimate of PV parameters. Methods: RTC-France PV cell, Photowatt-PWP 201 PV module, and KC200GT PV module were used as the case studies by using diode circuit models. The root mean square error (RMSE) between measured and estimated data was adopted to define PV parameter objective functions. A Friedman test was used to assess the reliability of algorithms. The parameter estimation results were cross-checked to confirm the accuracy of TADE algorithm performances. The PV module operating under various weather conditions was also performed to evaluate the TADE algorithm. Results and Discussion: The results verified that in most of the cases, the TADE algorithm surpassed other state-of-the-art optimization algorithms. For the double-diode model, the TADE algorithm obtained the RTC-France PV cell parameters with the RMSE value of 9.8243x10-04, the most accurate of all algorithms. Experimental results also showed that the TADE algorithm presented an excellent capability and accuracy in discovering the PV parameters and provided the best estimates for I-V and P-V experimental data of real PV cells and modules. Conclusions: The results have proven that the TADE algorithm has a great performance in terms of accuracy, reliability, and convergence speed for estimating PV parameters, even in different weather conditions

Determining physical and operational factors influencing pipeline leakage location pattern in water distribution networks using spatial poisson point process

There are four pillars in leakage management strategy for water distribution networks, consist of pressure management, speed of repairs, active leakage control, and asset management. Leakage can be managed by applying the right combination of those factors in the right leakage management strategy. This study conducted in Malang City, East Java, Indonesia. The factors that influence physical water loss in the water distribution system are analyzed by focusing on the physical and operational parameters. The data in this study consist of spatial data and quantitative data. Poisson distribution in the spatial point process is used as the leakage point distribution approximation. From the analysis, it is found that the distribution pattern of pipeline leakage location is categorized as inhomogeneous poisson process, with two types of intensity area. Based on the results of parameter estimation, it is found that the high-intensity area is affected by time of repairs, pipe diameter, pipe length ratio, water supply ratio, and pipe deterioration ratio but not affected by the hydraulic energy demand. While the low-intensity area is influenced by pipe diameter, pipe length ratio, water supply ratio, pipe deterioration ratio, and the hydraulic energy demand but not affected by the time of repairs.

Arrhythmogenic cardiomyopathy is characterized by apex-to-base heterogeneity of right ventricular myocardial contractility, stiffness, and mechanical delay: a patient-specific modeling study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NWO-ZonMw, VIDI grant 016.176.340 Dutch Heart Foundation (2015T082) Introduction Arrhythmogenic Cardiomyopathy (AC) is an inherited cardiac disease, characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Geno-positive subjects with and without symptoms may suffer from sudden cardiac death. Therefore, early disease detection and risk stratification is important. Right ventricular (RV) longitudinal deformation abnormalities in early stages of disease have been shown to be of prognostic value. We propose an imaging-based patient-specific computer modelling approach for non-invasive quantification of regional ventricular tissue abnormalities. Purpose To non-invasively reveal the individual patient’s myocardial tissue substrates underlying the regional RV deformation abnormalities in AC mutation carriers. Methods In 65 individuals carrying a plakophilin-2 or desmoglein-2 mutation and 20 control subjects, regional longitudinal deformation patterns of the RV free wall (RVfw), interventricular septum (IVS) and left ventricular free wall (LVfw) were obtained using speckle-tracking echocardiography (Figure: left). This cohort was subdivided into 3 consecutive clinical stages i.e. subclinical (concealed, n = 18) with no abnormalities, electrical stage (n = 13) with only electrocardiographic abnormalities, and structural stage (n = 34) with both electrical and structural abnormalities defined by the 2010 Task Force AC criteria. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects (Figure: middle). Using the individuals’ RVfw, IVS, and LVfw strain patterns as model input, this protocol automatically estimated regional RV and global IVS and LVfw tissue properties, such as myocardial contractility, stiffness, and activation delay. Results The computational model was able to reproduce the regional deformation patterns as measured clinically. Patient-specific parameter estimation results (Figure: right) revealed that clinical AC disease progression is characterized by a decrease in contractility and an increase in stiffness and mechanical delay of the RV myocardial tissue in the basal segment compared to the apex. The subclinical stage subjects showed tissue properties comparable to the control group, including a small apex-to-base heterogeneity in tissue properties. Conclusion Our patient-specific modelling approach is able to reveal individual myocardial substrates underlying the regional RV deformation abnormalities. Early abnormalities in RV longitudinal strain are most likely caused by increased heterogeneity in local tissue properties, such as an apex-to-base decrease of contractility, increased of myocardial stiffness, and time to peak stress. Abnormalities in tissue properties may be found already in the subclinical stage. In future studies, this artificial intelligence approach will be used to investigate how these abnormalities relate to disease progression and arrhythmogenic risk. Abstract Figure. Characterization of AC Disease Substrate

Coal Price Forecast Based on ARIMA Model

<div>This paper analyzes and determines the decision variables and constraints, establishes the EECM-ARAMA model to analyze and research coal price forecasts. Firstly, we first confirm the influencing factors. Then, we conduct correlation coefficient tests on price and various factors, and get the strength of the correlation between each factor and price. The second is to establish a coal price prediction model. Firstly, we use the EEMD method to transform the original price series into a stable time series, and then formulate three ARIMA models by comparing the size of the influencing factors and the parameter estimation results. After testing, we finally choose the ARIMA 03 model to predict the next 31 days, 35 Weekly and 36-month coal prices. Finally, we combine the models and ideas of the above issues to obtain factors that affect coal price changes and related price prediction models, and combine experience to give some feasible policy recommendations.</div>

The Effect of Global Awareness on Students' Reading Skill Based on Aksi Data

The Indonesian Student Competency Assessment (Asesmen Kompetensi Siswa Indonesia-AKSI) is a national survey aims to monitor the quality of education system across provinces. The results of the AKSI are expected to provide inputs on policies to improve the quality of learning outcomes in particular and the quality of education in general. The objective of AKSI is to measure students’ cognitive abilities, which include science, mathematics and reading. In addition, AKSI also collects data through questionnaires, one of which is about Global Awareness. The purpose of this study was to analyze the influence of student gender, parent’s education level, interest studying other cultures, adaptability and communication, understanding of global problems, tolerance, environmental awareness and school quality on students’ reading literacy skill. The data used in this study are data from the 2019 AKSI survey of 15.738 grade IX students and school accreditation of 1.805 junior high schools in Indonesia. The results of parameter estimation and hypothesis testing using the multilevel regression model concluded that gender, father's education, adaptability and communication skills, understanding of global problems, tolerance of religious and cultural differences and school quality affect students’ reading ability.

Model Regresi Cox Weibull Dengan Metode Penaksiran Parameter Efron Partial Likelihood

Survival analysis is an analysis that involves statistical tests to analyze data on the time or length of time until the occurrence of a particular event. One regression model for time duration data modeling is the Cox PH regression model. Cox PH regression applied to time duration data with Weibull distribution is called Cox PH Weibull regression. The purpose of this study is to obtain a Cox PH Weibull regression model and determine the factors that influence the length of treatment for tuberculosis patients. The parameter estimation in this study is Efron partial likelihood method. The Efron partial likelihood method is suitable for estimating Cox PH regression parameters to data containing ties. Based on the results of parameter estimation the best model is obtained by using AIC crtiteria. Based on the partial test, age is factor that influence to the length of treatment. The results of the study show that every increasing age patient one year, then length of treatment until the patient recovered, will increas 1,024 times.

Calibrating the Lee-Carter and the Poisson Lee-Carter Models Via Neural Networks

This paper introduces a neural network approach for fitting the Lee-Carter and the Poisson Lee-Carter model on multiple populations. We develop some neural networks that replicate the structure of the individual LC models and allow their joint fitting by analysing the mortality data of all the considered populations simultaneously. The neural network architecture is specifically designed to calibrate each individual model using all available information instead of using a population-specific subset of data as in the traditional estimation schemes. A large set of numerical experiments performed on all the countries of the Human Mortality Database (HMD) shows the effectiveness of our approach. In particular, the resulting parameter estimates appear smooth and less sensitive to the random fuctuations often present in the mortality rates' data, especially for low-population countries. In addition, the forecasting performance results significantly improved as well.

The Application of Item Response Theory in Analysis of Characteristics of Mathematical Literacy Test Items

This study aims to analyze the mathematical literacy abilities of students using IRT with the GPCM approach. The results of the model compatibility test show that the GPCM is suitable for analyzing mathematical literacy test instruments with 4 levels of mathematical literacy. The results of the item parameter estimation show that there is 1 level of mathematical literacy which has a high category for item discrimination, while the other 3 levels have a medium category. Based on item difficulty levels, 1 level of mathematical literacy has a difficult category, while 3 levels have easy categories. Most students are at the level of ability 0 < θ < 1, and there are 52% of students above the average mathematical literacy. Based on the test information function and the standard error shows that the mathematical literacy instrument is suitable for students with the ability -2 < θ < -0.5.

ESTIMASI CADANGAN KLAIM MENGGUNAKAN GENERALIZED LINEAR MODEL (GLM) DAN COPULA

In the articles of this will be discussed regarding the estimated reserves of the claim using the Generalized Linear Model (GLM) and Copula. Copula is a pair function distribution marginal becomes a function of distribution of multivariate. The use of copula regression in this article is to produce estimated reserves of claims. Generalized Linear Model (GLM) used as a marginal model for several lines of business. In research it is used three kinds of line of business that is individual, corporate and professional. The copula used is the Archimedean type of copula, namely Clayton and Gumbel copula. The best copula selection method is done using Akaike Information Criteria (AIC). Maximum Likelihood Estimation (MLE) is used to estimate copula parameters. The copula model used is the Clayton copula as the best copula. The parameter estimation results are used to obtain the estimated reserve value of the claim.

An R-shiny application to calculate optimal designs for single substance and interaction trials in dose response experiments

Background and objectiveOptimal experimental design theory proposes choosing specific settings in experimental trials in order to maximize the precision of the resulting parameter estimates. In dose response experiments, this corresponds to choosing the optimal dose levels for every available observation, and can be applied both to singular dose-response relationships and to interaction experiments where two substances are given simultaneously at several different mixture ratios (“ray designs”). While the theory of experimental design for this situation is well developed, the mathematical complexity prevents widespread use in practical applications. A simple to use application making the theory accessible to practitioners is thus very desirable. MethodsResults from established optimal experimental design theory are applied to dose response applications, focusing on log-logistic and Weibull class dose response functions. Suitable optimal design algorithms to solve these problems are implemented into an R-shiny based online application. ResultsThe application provides an interface to easily calculate D-optimal designs not only for singular dose experiments, but also for interaction trials with several combination rays of substances. Furthermore, the app also allows evaluating the efficiency of existing candidate designs, and finally allows construction of designs which perform robustly under different assumptions in regard to the true parameters.

Perceived Impact of Covid-19 Across Different Mental Disorders: A Study on Disorder-Specific Symptoms, Psychosocial Stress and Behavior.

The recent outbreak of the coronavirus disease (Covid-19) has plunged countries across the world into crisis. Both in the general population and in specific subgroups such as infected people or health care workers, studies have reported increased symptoms of anxiety, depression and stress. However, the reactions of individuals with mental disorders to Covid-19 have largely been neglected. The present study therefore aimed to investigate the perceived impact of Covid-19 and its psychological consequences on people with mental disorders. In this online survey, participants were asked to evaluate their disorder-specific symptoms, perceived psychosocial stress and behaviors related to Covid-19 in the current situation and retrospectively before the spread of Covid-19. The study included participants with self-identified generalized anxiety disorder (GAD), panic disorder and agoraphobia (PA), illness anxiety disorder (IA), social anxiety disorder (SAD), depression (DP), obsessive–compulsive disorder (OCD), body dysmorphic disorder (BDD), eating disorders (ED), schizophrenia spectrum and other psychotic disorders (SP), other non-specified mental disorder (other) as well as mentally healthy controls (HC). The results of bayesian parameter estimation suggest that the symptom severity of DP, GAD, IA and BDD has deteriorated as a reaction to Covid-19. Across all mental disorders and HC, self-reported psychosocial stress levels were higher during the outbreak of Covid-19 compared to before. A reduced frequency of social contacts and grocery shopping was found for all participants. People with self-identified mental disorders showed higher personal worries about Covid-19 and a higher fear of contagion with Covid-19 than did HC. According to our findings, Covid-19 may reinforce symptom severity and psychosocial stress in individuals with mental disorders. In times of pandemics, special support is needed to assist people with mental disorders and to prevent symptom deterioration.

Technical efficiency of Indian farmers in sugarcane production- A panel stochastic frontier approach

Aim: To estimate the technical inefficiency of Indian farmers in sugarcane production. Methodology: Although various stochastic frontier models for panel data are available, six of them were considered for the study, which is frequently applied in empirical research. Based on the log likelihood ratio, results are interpreted only for kumbhakar, lien and hardaker (KLH) model. Results: Indian farm?s overall technical efficiency ranged from minimum 0.11 to maximum 0.93 with a mean value of 0.70, it means that with same resource based average sugarcane growers can increase their yield up to 23% and most technical inefficient grower can increase their yield up to 82% in order to achieve overall technical efficiency level (93%) of efficient fellow grower in study area. Though, Tamil Nadu has the highest overall technical efficiency score in sugarcane production (0.76), the negative growth rate indicates its decline in technical efficiency. Haryana has a negative growth rate in persistent, residual and overall technical efficiency in sugarcane production. Interpretation: Among the seven states, Uttaranchal showed the highest scope of improving the sugarcane yield with 36%, followed by Uttar Pradesh (34%), Haryana (33%), Maharashtra (30%), Karnataka (30%), Andhra Pradesh (25%) and Tamil Nadu (24%). Results of parameter estimates confirmed that area in hectare, machine usage in hours, seed quantity in kilograms, manure usage in quintals and total fertilizer usage in kilograms are significant and positively influence the sugarcane production. The coefficients of dummy for farm size reveals that medium and large sugarcane farms are less efficient compared to small farms. Similarly, the share of own machine-hours has a negative impact on sugarcane production. Based on the empirical results appropriate policy is suggested for the study.

Arrhythmogenic cardiomyopathy is characterized by apex-to-base heterogeneity of right ventricular myocardial contractility and stiffness: an imaging-based patient-specific modeling study

Abstract Introduction Arrhythmogenic Cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Geno-positive subjects with and without symptoms may suffer from sudden cardiac death. Therefore, early disease expression and risk stratification is important. It has been shown that right ventricular (RV) longitudinal deformation abnormalities in early stages is related to disease progression. We propose an inverse patient-specific computer modelling approach, combined with clinical imaging data, to non-invasively quantify regional ventricular tissue abnormalities in AC mutation carriers. Purpose To non-invasively reveal the individual myocardial substrate underlying the regional RV deformation abnormalities in AC mutation carriers. Methods In 74 individuals carrying a plakophilin-2 or desmoglein-2 mutation, regional longitudinal deformation patterns of the RV free wall (RVfw), interventricular septum (IVS) and left ventricular free wall (LVfw) were obtained using speckle-tracking echocardiography (Figure: left column). This cohort was subdivided into 3 consecutive clinical stages i.e. subclinical (concealed, n=19) with no abnormalities, electrical stage (n=13) with only electrocardiographic abnormalities, and structural stage (n=42) with both electrical and structural abnormalities defined by the 2010 Task Force AC criteria. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects (Figure: middle column). Using the individuals' RV strain patterns as model input, this protocol automatically estimated regional RV tissue properties, such as myocardial contractility and stiffness. Results The computational model was able to reproduce the deformation as clinically measured. Patient-specific parameter estimation results (Figure: right column) revealed that clinical AC disease progression is characterized by an increase of base-to-apex heterogeneity in contractility and stiffness of the RV myocardial tissue, with a decreased contractility and an increased stiffness in the basal segment compared to the apex. Although this heterogeneity was most severe in the structural stage group, it was already present in many of the subjects in the subclinical stage. No clear apex-to-base heterogeneity of mechanical activation delay was found in this cohort. Conclusion Our patient-specific modelling approach showed that early abnormalities in RV longitudinal strain are most likely caused by increased heterogeneity in local tissue properties. Strain abnormalities are predominantly caused by decreased basal tissue contractility and increased basal tissue stiffness. Abnormalities in tissue properties may be found already in the subclinical stage. Future studies will investigate how these abnormalities relate to disease progression and arrhythmogenic risk. Characterization of AC Disease Substrate Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): This work was funded by the Netherlands Organisation for Scientific Research and the Dutch Heart Foundation.

Estimation of VECM Parameter Using Bayesian Approach: An Application to Analysis of Macroeconomic Variables

This study uses Bayesian approach to estimate Vector Error Correction Model (VECM). The aims of this study is to analyze the relationship between macroeconomic variables in Indonesia. To analyze the best method to influence government targets or policies on economic growth by studying the relationships of many macroeconomic variables. Previous studies in analyzing the relationship between macroeconomic variables with VECM analysis, using the Maximum Likelihood Estimation. However this estimation method cannot solve the problem of overparameterization in VECM model. The variables used in this study are six macroeconomic variables in Indonesia in 2010 quarter 1 to 2019 quarter 4 are GDP, the money supply, exchange rate of rupiah to US dollar, exports, imports and interest rates. The number of data in this study is less than the number of estimated parameters causing overparameterization problems. Therefore, this study uses the Bayesian parameter estimation method to avoid overparameterization problems in economic data. The model obtained from this study is the BVECM(3) and it has been proven that the model is suitable (the model diagnostic test). Based on the parameter estimation results of BVECM(3), the significant variables affecting GDP are GDP itself, the money supply, exchange rate of rupiah to US Dollar, exports, imports and the interest rate for Bank Indonesia Certificates. In addition, there is a two-way relationship that affects each other, namely the relationship between GDP and the money supply, exports and imports, exports and interest rates, and between imports and interest rates.

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

Quantum parameter estimation of the frequency and damping of a harmonic oscillator

We determine the quantum Cram\'er-Rao bound for the precision with which the oscillator frequency and damping constant of a damped quantum harmonic oscillator in an arbitrary Gaussian state can be estimated. This goes beyond standard quantum parameter estimation of a single mode Gaussian state for which typically a mode of fixed frequency is assumed. We present a scheme through which the frequency estimation can nevertheless be based on the known results for single-mode quantum parameter estimation with Gaussian states. Based on these results, we investigate the optimal measurement time. For measuring the oscillator frequency, our results unify previously known partial results and constitute an explicit solution for a general single-mode Gaussian state. Furthermore, we show that with existing carbon nanotube resonators (see J. Chaste et al.~Nature Nanotechnology 7, 301 (2012)) it should be possible to achieve a mass sensitivity of the order of an electron mass $\text{Hz}^{-1/2}$.

Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach

This paper develops a stochastic approach to impose regularity properties on a directional output distance function (DODF) and an output distance function, which can be estimated by maximum likelihood. We use the resulting parameter estimates to evaluate efficiency and total factor productivity (TFP) growth for Taiwan’s commercial banks over the period 2002–2015 and claim that the failure of considering the regularity restrictions and the exclusion of undesirables lead to miscalculated efficiency measures and productivity gains. The outcomes from the regularity-constrained DODF reveal that almost all data-points satisfy the regularity properties, that the managerial abilities of the banks improve after the subprime crisis of 2007, and that the sample banks’ TFP grow at an average rate of 1.93% per annum, whereby technical change is the driving force. However, our estimates show downward trends in the growth rate of TFP and technical change.

On the Effect of Asymmetrical Trait Inheritance on Models of Trait Evolution.

Current phylogenetic comparative methods modeling quantitative trait evolution generally assume that, during speciation, phenotypes are inherited identically between the two daughter species. This, however, neglects the fact that species consist of a set of individuals, each bearing its own trait value. Indeed, because descendent populations after speciation are samples of a parent population, we can expect their mean phenotypes to randomly differ from one another potentially generating a “jump” of mean phenotypes due to asymmetrical trait inheritance at cladogenesis. Here, we aim to clarify the effect of asymmetrical trait inheritance at speciation on macroevolutionary analyses, focusing on model testing and parameter estimation using some of the most common models of quantitative trait evolution. We developed an individual-based simulation framework in which the evolution of phenotypes is determined by trait changes at the individual level accumulating across generations, and cladogenesis occurs then by separation of subsets of the individuals into new lineages. Through simulations, we assess the magnitude of phenotypic jumps at cladogenesis under different modes of trait inheritance at speciation. We show that even small jumps can strongly alter both the results of model selection and parameter estimations, potentially affecting the biological interpretation of the estimated mode of evolution of a trait. Our results call for caution when interpreting analyses of trait evolution, while highlighting the importance of testing a wide range of alternative models. In the light of our findings, we propose that future methodological advances in comparative methods should more explicitly model the intraspecific variability around species mean phenotypes and how it is inherited at speciation.