Conducting Causal Analysis by Means of Approximating Probabilistic Truths

The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context.

A Generalized Non-Parametric Instrumental Variable-Control Function Approach to Estimation in Nonlinear Settings

When the endogenous variables enter non-parametrically into the regression equation standard linear instrumental variables approaches fail. Two existing solutions are the non-parametric instrumental variables (NPIVs) estimators, which are based on a set of conditional moment restrictions (CMRs), and the control function (CF) estimators, which use conditional mean independence (CMI) restrictions. Our first contribution is to show that – similar to CMI – the CMR place shape restrictions on the conditional expectation of the error given the instruments and endogenous variables that are sufficient for identification, and we call our new estimator based on these restrictions the CMR-CF estimator. Our second contribution is to develop an estimator for non-linear and non-parametric settings that can combine both CMR and CMI restrictions, which cannot be done in either the NPIV nor the non-parametric CF setting. This new “Generalized CMR-CF” uses both CMR and CMI restrictions together by allowing the conditional expectation of the structural error to depend on both instruments and control variables. When sieves are used to approximate both the structural function and the CF our estimator reduces to a series of least squares regressions. Our Monte Carlos illustrate that our new estimator performs well across several economic settings.

Worldline master formulas for the dressed electron propagator. Part 2. On-shell amplitudes

In the first part of this series, we employed the second-order formalism and the “symbol” map to construct a particle path-integral representation of the electron propagator in a background electromagnetic field, suitable for open fermion-line calculations. Its main advantages are the avoidance of long products of Dirac matrices, and its ability to unify whole sets of Feynman diagrams related by permutation of photon legs along the fermion lines. We obtained a Bern-Kosower type master formula for the fermion propagator, dressed with N photons, in terms of the “N-photon kernel,” where this kernel appears also in “subleading” terms involving only N − 1 of the N photons.In this sequel, we focus on the application of the formalism to the calculation of on-shell amplitudes and cross sections. Universal formulas are obtained for the fully polarised matrix elements of the fermion propagator dressed with an arbitrary number of photons, as well as for the corresponding spin-averaged cross sections. A major simplification of the on-shell case is that the subleading terms drop out, but we also pinpoint other, less obvious simplifications.We use integration by parts to achieve manifest transversality of these amplitudes at the integrand level and exploit this property using the spinor helicity technique. We give a simple proof of the vanishing of the matrix element for “all +” photon helicities in the massless case, and find a novel relation between the scalar and spinor spin-averaged cross sections in the massive case. Testing the formalism on the standard linear Compton scattering process, we find that it reproduces the known results with remarkable efficiency. Further applications and generalisations are pointed out.

Study of Wear Behaviour of Titanium Grade 2 Using Application of Taguchi Method

Titanium Grade 2 sample piece were subjected to wear on standard linear reciprocating tribometer machine for dry sliding condition in the constant temperature. Taguchi method were use to predict the wear rate against parameter such as Load, Frequency, and time. Signal to noise ratio and ANOVA were used study the impact of these three parameter on the wear rate. In conclusion, the major statistical factor affecting wear rate is load, followed by frequency and duration. Multiple linear regression equations are developed. Keywords: Titanium grade 2, wear rate, Tribometer, ANOVA

An application of Newton-like algorithm for H∞ proportional–integral–derivative controller synthesis of seesaw-cart system

This paper is concerned with the synthesis of proportional–integral–derivative (PID) controller according to the [Formula: see text] optimality criterion for seesaw-cart system. The equations of dynamics are obtained through modelling a seesaw-cart system actuated by direct-current motor via rack and pinion mechanism using the Euler–Lagrange approach. The obtained model is linearised and synthesis of the PID controller for linear model is performed. An algorithm based on the sub-gradient method, the Newton method, the self-adapting backpropagation algorithm and the Adams method is proposed to calculate the PID controller gains. The proposed control strategy is tested and compared with standard linear matrix inequality (LMI)-based method on computer simulations and experimentally on a laboratory model.

FORCE DRIVEN VIBRATIONS OF NONLINEAR PLATES ON A VISCOELASTIC WINKLER FOUNDATION UNDER THE HARMONIC MOVING LOAD

In the present paper, the forced driven nonlinear vibrations of an elastic plate in a viscoelastic medium and resting on a viscoelastic Winkler-type foundation are studied. The damping features of the surrounding medium and foundation are described by the Kelvin-Voigt model and standard linear solid model with fractional derivatives, respectively. The dynamic response of the plate is described by the set of nonlinear differential equations with due account for the fact that the plate is being under the conditions of the internal resonance accompanied by the external resonance. The expressions for the stress function and nonlinear coefficients for different types of boundary conditions are presented.

The ADR Encoder is a reliable and valid device to measure barbell mean velocity during the Smith machine bench press exercise

Velocity-based training is a contemporary resistance training method, which uses lifting velocity to prescribe and assess the effects of training. However, the high cost of velocity monitoring devices can limit their use among strength and conditioning professionals. Therefore, this study aimed to examine the reliability and concurrent validity of an affordable linear position transducer (ADR Encoder) for measuring barbell velocity during the Smith machine bench press exercise. Twenty-eight resistance-trained males performed two blocks of six repetitions in a single session. Each block consisted of two repetitions at 40%, 60%, and 80% of their estimated one-repetition maximum. The mean velocity of the lifting phase was simultaneously recorded with the ADR Encoder and a gold-standard linear velocity transducer (T-Force® System). Both devices demonstrated high reliability for measuring mean velocity (ADR Encoder: CVrange = 2.80%–6.40% and ICCrange = 0.78–0.82; T-Force® System: CVrange = 3.27%–6.62% and ICCrange = 0.77–0.81). The ADR Encoder provided mean velocity at 40%1RM with a higher reliability than the T-Force® System (CVratio = 1.17), but the reliability did not differ between devices at higher loads (60%1RM–80%1RM) (CVratio ≤ 1.08). No fixed or proportional bias was observed for the different loads using least-products regression analysis, while the Bland–Altman plots revealed low systematic bias (0.01 m·s−1) and random errors (0.03 m·s−1). However, heteroscedasticity of the errors was observed between both devices ( R2 = 0.103). The high reliability and validity place the ADR Encoder as a low-cost device for accurately measuring mean velocity during the Smith machine bench press exercise.

Structural Equation Modeling using Stata

In this tutorial, you will learn how to fit structural equation models (SEM) using Stata software. SEMs can be fit in Stata using the sem command for standard linear SEMs, the gsem command for generalized linear SEMs, or by drawing their path diagrams in the SEM Builder. After a brief introduction to Stata, the sem command will be demonstrated through a confirmatory factor analysis model, mediation model, group analysis, and a growth curve model, and the gsem command will be demonstrated through a random-slope model and a logistic ordinal regression. Materials and datasets are provided online, allowing anyone with Stata to follow along.

Using Predictive Analytics to Measure Effectiveness of Social Media Engagement: A Digital Measurement Perspective

As social media becomes an increasingly dominant and important component of sport organizations’ marketing and communication strategies, effective marketing measurement techniques are required. Using social media data of a Division I football team, this research demonstrates how predictive analytics can be used as a marketing measurement tool. A support vector machine model was compared to a standard linear regression with respect to accurately predicting Facebook posts’ total interactions. The predictive model was used as (i) a planning tool to forecast future post engagement based on a variety of post characteristics and (ii) an evaluation tool of a marketing campaign by providing accurate benchmarks to compare against achieved engagement metrics. Results indicated the support vector machine model outperformed the standard linear regression and the marketing campaign was unsuccessful in achieving its goals. This research provides a foundation for future use of predictive analytics in social media and sport management scholarship

Jamming and Anti-Jamming Strategies of Mobile Vehicles

Anti-jamming games have become a popular research topic. However, there are not many publications devoted to such games in the case of vehicular ad hoc networks (VANETs). We considered a VANET anti-jamming game on the road using a realistic driving model. Further, we assumed the quadratic power function in both vehicle and jammer utility functions instead of the standard linear term. This makes the game model more realistic. Using mathematical methods, we expressed the Nash equilibrium through the system parameters in single-channel and multi-channel cases. Since the network parameters are usually unknown, we also compared the performance of several reinforcement learning algorithms that iteratively converge to the Nash equilibrium predicted analytically without having any information about the environment in the static and dynamic scenarios.