Residual Error Term Research Articles

IFRS Adoptions on Entrepreneurial Intentions: The Nigeria Experience from 2006-2017

This study investigated pre and post-IFRS adoption and entrepreneurial intentions from 2006-2017 using Panel Data from annual financial statement of quoted companies. Data for the study were obtained from secondary sources and analysed using Eview9 statistical package namely: ADF Unit Root Test, Panel Cointegration test; Bayesian Error Correction Model, Panel OLS among others. Data for the work were drawn with purposive sampling techniques from samples of 72 observations Nigeria stock exchange statistical Bulletins. The R-square (Coefficient of Determination) shows an explanation of Pre IFRS (0.280213) and Post IFRS (0.440826) percent of the employed model, i.e. Pre IFRS=28%; Post IFRS 44% and of the variance of the criterion variable is explained by the predictor variable. Which shows how well-fitted the regression is based on the unique theoretical combinations of variables. The Durbin-Watson being a test for auto/serial correlation shows an output of Post IFRS is 1.434594, while Pre IFRS is 1.428219 which signifies the absence of a serial or autocorrelation of the residual (error term) and appears to be within the relevant range. The F-statistics testing the overall hypothesis of the slope coefficient displays a very significant long run relationship based on the statistic output of Post IFRS (6.109727); Pre IFRS (0.004044) at a probability level of Post IFRS (0.000957); Pre IFRS (6.423459) which is less than the critical value of 0.05 (0.00 < 0.05). Therefore, overall we reject null hypothesis and alternate that there is significant (short-run and long-run) relationship between employed variables but in favour with Post IFRS adoption on entrepreneurial intentions. We therefore recommend among others that entrepreneurial intention should focus to boost the long term benefits of all total asset, current asset, non-current asset and profitability index in line with IFRS value relevance reporting accounting techniques.

Long Time Asymptotic Behavior for the Nonlocal mKdV Equation in Solitonic Space–Time Regions

We study the long time asymptotic behavior for the Cauchy problem of an integrable real nonlocal mKdV equation with nonzero initial data in the solitonic regions \begin{align*} &q_t(x,t)-6\sigma q(x,t)q(-x,-t)q_{x}(x,t)+q_{xxx}(x,t)=0, &q(x,0)=q_{0}(x),\ \ \lim_{x\to \pm\infty} q_{0}(x)=q_{\pm}, \end{align*} where $|q_{\pm}|=1$ and $q_{+}=\delta q_{-}$, $\sigma\delta=-1$. In our previous article, we have obtained long time asymptotics for the nonlocal mKdV equation in the solitonic region $-6<\xi<6$ with $\xi=\frac{x}{t}$. In this paper, we calculate the asymptotic expansion of the solution $q(x,t)$ for other solitonic regions $\xi<-6$ and $\xi>6$. Based on the Riemann-Hilbert problem of the the Cauchy problem, further using the $\bar{\partial}$ steepest descent method, we derive different long time asymptotic expansions of the solution $q(x,t)$ in above two different space-time solitonic regions. In the region $\xi<-6$, phase function $\theta(z)$ has four stationary phase points on the $\mathbb{R}$. Correspondingly, $q(x,t)$ can be characterized with an $\mathcal{N}(\Lambda)$-soliton on discrete spectrum, the leading order term on continuous spectrum and an residual error term, which are affected by a function ${\rm Im}\nu(\zeta_i)$. In the region $\xi>6$, phase function $\theta(z)$ has four stationary phase points on $i\mathbb{R}$, the corresponding asymptotic approximations can be characterized with an $\mathcal{N}(\Lambda)$-soliton with diverse residual error order $\mathcal{O}(t^{-1})$.

Systematic Ionospheric Residual Errors in GNSS Radio Occultation: Theory for Spherically Stratified Media

AbstractThe standard ionospheric correction of bending angles in Global Navigation Satellite System (GNSS) radio occultation (RO) measurements removes most of the influence from the ionosphere, but leaves small systematic residual errors in the corrected data. The main reasons for residual errors at stratospheric and mesospheric altitudes are the neglect of higher‐order terms in the expansion of the ionospheric refractive index, and the fact that the two GNSS signals follow slightly different paths through the ionosphere. Another reason is the neglect of the local electron density at the receiver in orbit. These residual errors depend on the geomagnetic field and the spatial distribution of the ionospheric electron density and its gradients. In this work we derive this dependency to high accuracy for a spherically stratified ionosphere, including the “bi‐local” situation where the ionosphere is different (though still spherically stratified) on the inbound and outbound side of the RO event tangent points. As part of the derivations, we find a small residual error term not previously noted, which can become appreciable for elliptical satellite orbits. The results are verified by ray tracing through simple ionospheric and geomagnetic field models. The accuracy of a higher‐order bi‐local ionospheric residual error correction based on these results would be limited by the uncertainty in knowledge of the electron density and by horizontal electron density gradients along the ray paths.

A novel analytical algorithm for generalized fifth-order time-fractional nonlinear evolution equations with conformable time derivative arising in shallow water waves

The purpose of this research is to study, investigate, and analyze a class of temporal time-FNEE models with time-FCDs that are indispensable in numerous nonlinear wave propagation phenomena. For this purpose, an efficient semi-analytical algorithm is developed and designed in view of the residual error terms for solving a class of fifth-order time-FCKdVEs. The analytical solutions of a dynamic wavefunction of the fractional Ito, Sawada-Kotera, Lax’s Korteweg-de Vries, Caudrey-Dodd-Gibbon, and Kaup-Kupershmidt equations are provided in the form of a convergent conformable time-fractional series. The related consequences are discussed both theoretically as well as numerically considering the conformable sense. In this direction, convergence analysis and error estimates of the developed algorithm are studied and analyzed as well. Concerning the considered models, specific unidirectional physical experiments are given in a finite compact regime to confirm the theoretical aspects and to demonstrate the superiority of the novel algorithm compared to the other existing numerical methods. Moreover, some representative results are presented in two- and three-dimensional graphs, whilst dynamic behaviors of fractional parameters are reported for several α values. From the practical viewpoint, the archived simulations and consequences justify that the iterative algorithm is a straightforward and appropriate tool with computational efficiency for long-wavelength solutions of nonlinear time-FPDEs in physical phenomena.

A virtual force method to rectify the equation of state of the lattice Boltzmann models

In this work, to rectify the equation of state (EOS) of a recently introduced constant speed entropic kinetic model (CSKM), a virtual force method is proposed. The CSKM, as shown in Zadehgol and Ashrafizaadeh [J. Comp. Phys. 274, 803 (2014)] and Zadehgol [Phys. Rev. E 91, 063311 (2015)], is an entropic kinetic model with unconventional entropies of Burg and Tsallis. The dependence of the pressure on the velocity, in the CSKM, was addressed and it was shown that it can be rectified by inserting rest particles into the model. This work shows that this dependence can also be removed by treating the pressure gradient as a pseudo force term, expanding the source term using the Fourier series, and applying the modified method of Khazaeli et al. [Phys. Rev. E 98, 053303 (2018)]. The proposed method can potentially be used to remove other pseudo-force error terms of the CSKM, e.g. the residual error terms which become significant at high Mach numbers, ensuring thermodynamic consistency of the entropic model, at the compressible flow regimes. The accuracy of the method is verified by simulating benchmark flows.

Optimized Refinement for Spatially Adaptive SPH

In this article, we propose an improved refinement process for the simulation of incompressible low-viscosity turbulent flows using Smoothed Particle Hydrodynamics, under adaptive volume ratios of up to 1 : 1, 000, 000. We derive a discretized objective function, which allows us to generate ideal refinement patterns for any kernel function and any number of particles a priori without requiring intuitive initial user-input. We also demonstrate how this objective function can be optimized online to further improve the refinement process during simulations by utilizing a gradient descent and a modified evolutionary optimization. Our investigation reveals an inherent residual refinement error term, which we smooth out using improved and novel methods. Our improved adaptive method is able to simulate adaptive volume ratios of 1 : 1, 000, 000 and higher, even under highly turbulent flows, only being limited by memory consumption. In general, we achieve more than an order of magnitude greater adaptive volume ratios than prior work.

The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere

Abstract. We present top-of-atmosphere and surface radiative kernels based on the atmospheric component (GA7.1) of the HadGEM3 general circulation model developed by the UK Met Office. We show that the utility of radiative kernels for forcing adjustments in idealised CO2 perturbation experiments is greatest where there is sufficiently high resolution in the stratosphere in both the target climate model and the radiative kernel. This is because stratospheric cooling to a CO2 perturbation continues to increase with height, and low-resolution or low-top kernels or climate model output are unable to fully resolve the full stratospheric temperature adjustment. In the sixth phase of the Coupled Model Intercomparison Project (CMIP6), standard atmospheric model data are available up to 1 hPa on 19 pressure levels, which is a substantial advantage compared to CMIP5. We show in the IPSL-CM6A-LR model where a full set of climate diagnostics are available that the HadGEM3-GA7.1 kernel exhibits linear behaviour and the residual error term is small, as well as from a survey of kernels available in the literature that in general low-top radiative kernels underestimate the stratospheric temperature response. The HadGEM3-GA7.1 radiative kernels are available at https://doi.org/10.5281/zenodo.3594673 (Smith, 2019).

An improved dealiasing scheme for the fourth‐order Runge‐Kutta method: Formulation, accuracy and efficiency analysis

SummaryIn this paper, the Random Phase Shift Method (RPSM) dealiasing scheme has been developed with the classical fourth‐order explicit Runge‐Kutta (RK4) method. This scheme is implemented in different benchmark problems to verify its numerical accuracy and computational efficiency where strong gradients are present in the solution. The propagation of aliasing errors through the substeps of RK4 is derived to show the existence of the residual aliasing error terms which results in mild smoothing effect without dissipating the small‐scale flow structures. Smoothness and numerical stability in the solutions obtained from the RPSM scheme also remain well preserved even at under‐resolved conditions. Numerical results agree well with the analytical and the computed solutions from previous studies. RPSM scheme shows a slight delay in the formation of numerical singularity for the inviscid flows but the filtering‐based schemes suffer from early blow‐up problem. We observe that this scheme displays better resolving ability than higher‐order exponential smoothing spectral filter scheme in capturing the strong fronts accurately even at just resolved spatial grid resolutions. Three‐dimensional truncation‐based dealiasing scheme, spherical truncation (SPT) shows vortices generated due to the parasitic currents in the solution of the inviscid three‐dimensional Taylor Green (TG) vortex flows. RPSM displays only the accurate isocontours of vortical field at nearly same computational expenses as the SPT scheme.

The Autonomic Progress Bar Motivates Treatment Completion for Patients of Stimulant Use Disorder and Cannabis Use Disorder.

Background: The intrinsic motivation behind the “need to complete” is more influential than external incentives. We introduced a novel progress-bar tool to motivate the completion of programs designed to treat stimulant and cannabis use disorders. We further examined the effectiveness of the progress bar's scoring approach in forecasting consistently negative urine tests. Methods: This study's participants included 568 patients with stimulant, amphetamine-type, and cannabis use disorders who were undergoing 12-month mandatory treatment programs at Taichung Veterans General Hospital in Taiwan. Patients were given scores of 1, -1, or 0 depending on whether they received negative, positive, or missing urinalysis reports, respectively. The autonomic progress bar generated weekly score totals. At the group level, scorei donated scores from all patients for a given week (i denoted the week). Scorei was standardized to adjusted scorei. We then conducted Autoregressive Integrated Moving Average (ARIMA) Model of time-series analyses for the adjusted scorei. Results: A total of 312 patients maintained treatment progress over the 12-month program. The autonomic score calculator totaled the shared achievements of these patients. The coefficients of the lag variables for mean (p), lag variables for residual error term (q), and number of orders for ensuring stationary (d) were estimated at p = 3, d = 4, and q = 7 for the first half of the treatment program, and were estimated at p = 2, d = 2, and q = 3 for the second half. Both models were stationary and tested as fit for prediction (p < 0.05). Sharply raised adjusted scores were predicted during the high-demand treatment phase. Discussion: This study's novel progress-bar tool effectively motivated treatment completion. It was also effective in forecasting continually negative urine tests. The tool's free open-source code makes it easy to implement among many substance-treatment services.

An adaptive fractional calculus image denoising algorithm in digital reflection dispensation

Image de-noising has become an important step in digital image processing and removing unwanted noisy from the image is important area of the research. The project assign to generate the noise free images from the noisy images has consider the three objective which are (1) suppression of the noise effectively in uniform regions, (2) preserve edges and other similar image characteristics and (3) provide a visually natural appearance. In this project the hybrid nature of technique is used which include fractional order for determination diffusion coefficients and a residual error term and wavelet transform method for decomposition of images into low and high frequency. The diffusion coefficients can be used effectively for noise removal and the residual error term can help to prevent image distortion. In this project simulated performance of the image de-noising are done using MATLAB and peak signal-to-noise ratio (PSNR), Normalized Cross Correlation (NCC), Normalized Absolute Error (NAE) and Structural Content (SC) are used to evaluate the method.

How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits.

Baseline correction plays an important role in past and current methodological debates in ERP research (e.g., the Tanner vs. Maess debate in the Journal of Neuroscience Methods), serving as a potential alternative to strong high-pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, implying a reduction in the signal-to-noise ratio. In other words, traditional baseline correction is statistically unnecessary and even undesirable. Including the baseline interval as a predictor in a GLM-based statistical approach allows the data to determine how much baseline correction is needed, including both full traditional and no baseline correction as special cases. This reduces the amount of variance in the residual error term and thus has the potential to increase statistical power.

Development of Probabilistic Dam Breach Model Using Bayesian Inference

AbstractDam breach models are commonly used to predict outflow hydrographs of potentially failing dams and are key ingredients for evaluating flood risk. In this paper a new dam breach modeling framework is introduced that shall improve the reliability of hydrograph predictions of homogeneous earthen embankment dams. Striving for a small number of parameters, the simplified physics‐based model describes the processes of failing embankment dams by breach enlargement, driven by progressive surface erosion. Therein the erosion rate of dam material is modeled by empirical sediment transport formulations. Embedding the model into a Bayesian multilevel framework allows for quantitative analysis of different categories of uncertainties. To this end, data available in literature of observed peak discharge and final breach width of historical dam failures were used to perform model inversion by applying Markov chain Monte Carlo simulation. Prior knowledge is mainly based on noninformative distribution functions. The resulting posterior distribution shows that the main source of uncertainty is a correlated subset of parameters, consisting of the residual error term and the epistemic term quantifying the breach erosion rate. The prediction intervals of peak discharge and final breach width are congruent with values known from literature. To finally predict the outflow hydrograph for real case applications, an alternative residual model was formulated that assumes perfect data and a perfect model. The fully probabilistic fashion of hydrograph prediction has the potential to improve the adequate risk management of downstream flooding.

097 Identifying novel acoustic emission biomarkers for use in knee osteoarthritis clinical trials

Background: The recent development of techniques to measure high-frequency acoustic emission (AE) from knees has opened up the possibility of identifying AE features which reflect the integrity of interactions between joint components during weight bearing movement. The objective of this study was to determine whether novel candidate biomarkers, based on AE measurements, can be identified for future use in clinical trials and stratified medicine applications for knee osteoarthritis (OA). We focused on testing the reproducibility of candidate AE biomarkers and their association with other markers, using a cohort approach. Methods: Participants with knee pain and KL scores between 1-4 were recruited from local NHS primary and secondary care organisations. AE data were collected and analysed from the worse knee, as identified by each participant, using our established sit-stand-sit movement protocol. Variation in AE measurement was analysed in 45 participants by fitting two linear mixed effects models. In addition to random effects terms for practitioners and visits, respectively, both models included a fixed effect term for the AE machines and random effects terms to capture between-participant variability and residual error. Associations with other markers were tested in 68 participants by fitting a linear mixed effects model for each candidate biomarker. The model included fixed effects for the covariates of interest, a participant-specific random effect that accounts for correlation between repeated measurements within the same individual, and a residual error term. A multiple regression model was developed using forward selection based on the likelihood ratio test with a cut-off for significance of p < 0.1. Results: Of four candidate AE biomarkers tested, AE number of hits showed the best reproducibility profile regarding variation within session, day to day, week to week, between practitioner, and between machines. AE number of hits was higher in people with KL2, 3 or 4 scores than in those with KL1. Hits occurred predominantly in two of the four pre-defined sit-stand movement quadrants. AE number of hits also showed significant associations with contralateral knee pain, and with body weight. The protocol used was feasible and acceptable to all participants and health professionals involved. Conclusion: Measurement of AE hits using a simple sit-stand-sit movement protocol offers a novel and convenient approach for assessing the integrity of interactions between joint components during weight bearing movement. AE number of hits offers a novel potential knee OA biomarker for use in large multicentre clinical trials of knee OA treatments. AE measurement may reflect a composite of knee structural changes and joint loading factors. Refinement of the method may further strengthen the utility of the AE approach for clinical trials. This approach may also have applications for monitoring knee OA in primary care.

Image Denoising Based on Adaptive Fractional Order with Improved PM Model

In order to improve the image quality, in this paper, we propose an improved PM model. In the proposed model, we introduce two novel diffusion coefficients and a residual error term and replace the integer differential operator with the fractional differential operator in the PM model. The diffusion coefficients can be used effectively for edge detection and noise removal. The residual error term can help to prevent image distortion. Fractional order differential operator has a good characteristic that it can enhance image texture information while removing image noise. Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter k, and it does not need to do any experiments to get the value of k. For the computing fractional order diffusion coefficient, we employ the discrete Fourier transform, and an iterative scheme is carried out in the frequency domain. In the proposed model, not only is the integer differential operator replaced with the fractional differential operator, but also the order of the fractional differentiation is determined adaptively with the local variance. Comparing with some existing models, the experimental results show that the proposed algorithm can not only better suppress noise, but also better preserve edge and texture information. Moreover, the running time is greatly reduced.

An alternative to post hoc model modification in confirmatory factor analysis: The Bayesian lasso.

As a commonly used tool for operationalizing measurement models, confirmatory factor analysis (CFA) requires strong assumptions that can lead to a poor fit of the model to real data. The post hoc modification model approach attempts to improve CFA fit through the use of modification indexes for identifying significant correlated residual error terms. We analyzed a 28-item emotion measure collected for n = 175 participants. The post hoc modification approach indicated that 90 item-pair errors were significantly correlated, which demonstrated the challenge in using a modification index, as the error terms must be individually modified as a sequence. Additionally, the post hoc modification approach cannot guarantee a positive definite covariance matrix for the error terms. We propose a method that enables the entire inverse residual covariance matrix to be modeled as a sparse positive definite matrix that contains only a few off-diagonal elements bounded away from zero. This method circumvents the problem of having to handle correlated residual terms sequentially. By assigning a Lasso prior to the inverse covariance matrix, this Bayesian method achieves model parsimony as well as an identifiable model. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed procedure. (PsycINFO Database Record

Physically consistent data assimilation method based on feedback control for patient‐specific blood flow analysis

This paper presents a novel data assimilation method for patient-specific blood flow analysis based on feedback control theory called the physically consistent feedback control-based data assimilation (PFC-DA) method. In the PFC-DA method, the signal, which is the residual error term of the velocity when comparing the numerical and reference measurement data, is cast as a source term in a Poisson equation for the scalar potential field that induces flow in a closed system. The pressure values at the inlet and outlet boundaries are recursively calculated by this scalar potential field. Hence, the flow field is physically consistent because it is driven by the calculated inlet and outlet pressures, without any artificial body forces. As compared with existing variational approaches, although this PFC-DA method does not guarantee the optimal solution, only one additional Poisson equation for the scalar potential field is required, providing a remarkable improvement for such a small additional computational cost at every iteration. Through numerical examples for 2D and 3D exact flow fields, with both noise-free and noisy reference data as well as a blood flow analysis on a cerebral aneurysm using actual patient data, the robustness and accuracy of this approach is shown. Moreover, the feasibility of a patient-specific practical blood flow analysis is demonstrated.

Genetic and environmental factors affecting 305-day mature equivalent milk yield of Holstein Friesian cows in the United Arab Emirates

The main objective of this research was determining the effect of sire of cow, year and season of freshening, lactation number, and their different interactions on 305-day mature equivalent (ME) milk yield of Holstein Friesian cows in the United Arab Emirates (UAE). It also aimed to estimate sire and error variance and heritability of 305-day ME milk yield. The data was collected from AL Salamat Dairy Farm in Al Ain. The average size of the herd was 1000 animals during the experimental period. The Dairy Comp 305 computer program was used for compiling the data. The data under the study covered the years 2004 to 2007. The climatic temperature during the summer (April to September) varied from moderate to very hot. The winter (October to March) had lower temperature and higher relative humidity than the summer. All the effects were fixed except for random sire and residual error term. The overall least-square mean of the 305-day ME milk yield was 11060Å}355 kg/305 days. The data showed a significant effect of sire of cow (p≤0.001), year of freshening (p≤0.001), lactation number (p≤0.01), year x season of freshening (p≤0.01), and year of freshening x lactation number (p≤0.001) on milk yield. However, the effect of season of freshening showed non significant effect. No significant seasonal differences were found in 2004. However, in the year 2005 and 2006, the summer fresheners had significantly higher yield than those of the winter season. The 305-day ME pertaining to summer vs. winter were 11691Å}400 kg vs. 11483Å}410 kg and 11522Å}369 kg vs. 11041Å}374 kg for the year 2005 and 2006, respectively. However, the reverse was true in 2007 with the fresheners during summer that have lower 305-day ME (10286Å}372kg) than those freshening during winter (10672Å}388 kg). The differences among the three lactations were relatively high in 2004 (9837 to 12116 kg), compared to 2005 - 2006 (11267 to 11747 kg) and 2007 (9853 to 11355 kg). The heritability of 305-day ME was 0.31 indicating that a significant response to selection would be achieved through a well-designed progeny testing and cow evaluation program.

SYSTEMATIC EFFECTS IN POLARIZING FOURIER TRANSFORM SPECTROMETERS FOR COSMIC MICROWAVE BACKGROUND OBSERVATIONS

The detection of the primordial B-mode polarization signal of the cosmic microwave background (CMB) would provide evidence for inflation. Yet as has become increasingly clear, the detection of a such a faint signal requires an instrument with both wide frequency coverage to reject foregrounds and excellent control over instrumental systematic effects. Using a polarizing Fourier transform spectrometer (FTS) for CMB observations meets both these requirements. In this work, we present an analysis of instrumental systematic effects in polarizing Fourier transform spectrometers, using the Primordial Inflation Explorer (PIXIE) as a worked example. We analytically solve for the most important systematic effects inherent to the FTS - emissive optical components, misaligned optical components, sampling and phase errors, and spin synchronous effects - and demonstrate that residual systematic error terms after corrections will all be at the sub-nK level, well below the predicted 100 nK B-mode signal.

Residual feed intake is repeatable for lactating Holstein dairy cows fed high and low starch diets

Residual feed intake (RFI) is a tool to quantify feed efficiency in livestock and is commonly used to assess feed efficiency independent of production level, body weight (BW), or BW change. Lactating Holstein cows (n=109; 44 primiparous and 65 multiparous), averaging (mean ± standard deviation, SD) 665±77kg of BW, 42±9kg of milk/d, and 120±30 d postpartum, were fed diets of high (HI) or low (LO) starch content in 4 crossover experiments with two 28-d treatment periods. The LO diets were ~40% neutral detergent fiber (NDF) and ~14% starch and the HI diets were ~26% NDF and ~30% starch. Individual dry matter intake (DMI) of a cow was modeled as a function of milk energy output, metabolic BW, body energy change, and fixed effects of parity, experiment, cohort nested within experiment, and diet nested within cohort and experiment; RFI for each cow was the residual error term. Cows were classified as high (>0.5 SD of the mean), medium (±0.5 SD of the mean), or low (<−0.5 SD of the mean) RFI. On average, for the linear model used to determine RFI for individual cows, each unit increase in milk energy output, metabolic BW, or body energy gain was associated with 0.35, 0.09, or 0.05kg increase in DMI, respectively. When compared with LO diets, HI diets increased energy partitioning to body energy gain and tended to increase DMI. The correlation between RFI when cows were fed HI diets and RFI when cows were fed LO diets was 0.73 and was similar across each parity and experiment. Fifty-six percent of cows maintained the same RFI classification (high, medium, or low RFI) and only 4 of 109 cows changed from high RFI to low RFI or vice versa when diets were changed. Milk:feed, income over feed cost, and DMI were also highly repeatable (r=0.72, 0.84, and 0.92, respectively). We achieved significant changes in milk yield and component concentration as well as energy partitioning between HI and LO diets and still determined RFI to be repeatable across diets. We conclude that RFI is reasonably repeatable for a wide range of dietary starch levels fed to mid-lactation cows, so that cows that have low RFI when fed high corn diets will likely also have low RFI when fed diets high in nonforage fiber sources.

The Missing Main Effect of Welfare State Regimes: A Replication of ‘Social Policy Responsiveness in Developed Democracies’ by Brooks and Manza

This article reports the results of a replication of Brooks and Manza’s “Social Policy Responsiveness in Developed Democracies” published in 2006 in the American Sociological Review. The article finds that Brooks and Manza utilized an interaction term but excluded the main effect of one of the interacted variables. This model specification has specific implications: statistically, that the omitted main effect variable has no correlation with the residual error term from their regression; theoretically speaking, this means that all unobserved historical, cultural, and other characteristics that distinguish liberal democratic welfare regimes from others can be accounted for with a handful of quantitative measures. Using replicated data, this article finds that the Brooks and Manza models fail these assumptions. A sensitivity analysis using more than 800 regressions with different configurations of variables confirms this. In 99.5 percent of the cases, addition of the main effect removes Brooks and Manza’s empirical findings completely. A theoretical discussion illuminates why these findings are not surprising. This article provides a reminder that models and theories are coterminous, each implied by the other.