Negative Variance Research Articles

Correlation of the Lateral Wrist Radiograph to Ulnar Variance: A Cadaveric Study.

Both positive and negative ulnar variance have been implicated in a variety of wrist disorders. Surgery aims to correct the variance in these pathologic conditions. This necessitates accurate and reproducible measuring tools; however, the most accurate radiographic measurement technique remains unclear. The purposes of this study were to evaluate 3 methods for determining ulnar variance and to compare each with direct anatomic measurement in a cadaver model. We fixed 10 fresh above-elbow cadaver specimens in neutral rotation and obtained standardized fluoroscopic posteroanterior and lateral wrist images. A dorsal approach was performed and two independent investigators directly measured ulnar variance using digital calipers with the cartilage both intact and denuded. Ulnar variance was measured radiographically using the lateral, perpendicular, and central reference point methods. The reliability of each set of measurements (within a 1-mm cutoff) was assessed by the intraclass coefficient; agreement between radiographic and direct measurements was evaluated by the Bland-Altman method. Each method of determining ulnar variance demonstrated near perfect agreement by the intraclass coefficient. The lateral radiograph method correlated highly with the directly measured ulnar variance with the cartilage denuded with an average measurement difference of 0.06 mm. No radiographic measurement technique demonstrated consistent agreement within 1 mm of the measured ulnar variance with the cartilage intact. Ulnar variance measured by the lateral wrist radiograph technique correlates highly with the directly measured osseous ulnar variance. The remaining measurement techniques did not correlate reliably to within 1 mm of the directly measured ulnar variance with 95% confidence. No method was able to account accurately for the articular cartilage thicknesses at the lunate facet of the radius or the distal ulnar head, which we found to vary in an unpredictable manner. Whereas the lateral radiograph has been shown to allow for more reliable standardization of wrist position compared with the posteroanterior view, this study also highlights the inherent limitations of using static radiographic images in evaluating ulnar variance. The results of the current study demonstrate the utility of the lateral wrist radiograph for assessing bony ulnar variance.

Modeling and implementation of local volatility surfaces in Bayesian framework

In this study, we focus on the reconstruction of volatility surfaces via a Bayesian framework. Apart from classical methods, such as, parametric and non-parametric models, we study the Bayesian analysis of the (stochastically) parametrized volatility structure in Dupire local volatility model. We systematically develop and implement novel mathematical tools for handling the classical methods of constructing local volatility surfaces. The most critical limitation of the classical methods is obtaining negative local variances due to the ill-posedness of the numerator and/or denominator in Dupire local variance equation. While several numerical techniques, such as Tikhonov regularization and spline interpolations have been suggested to tackle this problem, we follow a more direct and robust approach. With the Bayesian analysis, choosing a suitable prior on the positive plane eliminates the undesired negative local variances.

Positive and Negative Emotions and Nonattachment in Vietnamese Buddhists

Several research has shown that nonattachment, a Buddhist practice, could reduce negative emotions and improve positive emotions (Sahdra & Shaver, 2013; Sahdra, Shaver, & Brown, 2010; Wang, Wong, & Yeh, 2016; Wendling, 2012). We aimed to explore such influences in a sample of Vietnamese Buddhists (N = 472). Our methods included the State Trait Anxiety Inventory (STAI; Spielberger et al., 1983), the Nonattachment Scale (NAS; Sahdra et al., 2010), and a demographic and religious questionnaire. Results showed that positive emotion scores (M = 2.89, SD = .502) of participants were significantly higher than that of their negative emotion scores (M = 2.10, SD = .587, p < .001). People with strong religious commitment such as monks, lay-people who practiced at pagoda, and lay-people who practiced with sangha had higher positive emotion scores and less negative emotion scores than those whose religious commitment were weak. Nonattachment was positively correlated with positive emotions (r = .47, p < .01) and negatively correlated with negative emotions (r = -.37, p < .01). Nonattachment could also explain 21.7% positive emotions variance (p < .001) and 12.4% negative emotions variance (p < .001). Nonattachment and religious commitment could, therefore, influence greatly positive emotions in Buddhists. This result suggested a discussion about applying nonattachment to prevent emotional problems and improve psychological well-being.

Kriging models for linear networks and non‐Euclidean distances: Cautions and solutions

AbstractThere are now many examples where ecological researchers used non‐Euclidean distance metrics in geostatistical models that were designed for Euclidean distance, such as those used for kriging. This can lead to problems where predictions have negative variance estimates. Technically, this occurs because the spatial covariance matrix, which depends on the geostatistical models, is not guaranteed to be positive definite when non‐Euclidean distance metrics are used. These are not permissible models, and should be avoided.I give a quick review of kriging and illustrate the problem with several simulated examples, including locations on a circle, locations on a linear dichotomous network (such as might be used for streams), and locations on a linear trail or road network. I re‐examine the linear network distance models from Ladle, Avgar, Wheatley, and Boyce (,Methods in Ecology and Evolution, 8, 329) and show that they are not guaranteed to have a positive definite covariance matrix.I introduce the reduced‐rank method, also called a predictive‐process model, for creating valid spatial covariance matrices with non‐Euclidean distance metrics. It has an additional advantage of fast computation for large datasets.I re‐analysed the data of Ladle et al. (,Methods in Ecology and Evolution, 8, 329), showing that fitted models that used linear network distance in geostatistical models, both with and without a nugget effect, had negative variances, poor predictive performance compared with reduced‐rank methods, and had improper coverage for the prediction intervals. The reduced‐rank approach using linear network distances provided a class of permissible models that had better predictive performance and proper coverage for the prediction intervals, and could be combined with Euclidean distance models to provide the best overall predictive performance.

Maximizing College Savings through the Selection of Low-Cost 529 Plan Portfolios

Parents and grandparents saving for descendent children and grandchildren’s college expenses seek to leave as large a nest egg as possible. As with most investments, the objective is to reach a high terminal value without taking excessive risk. In the case of college savings funds, a larger terminal value will expand the set of options available to the college-bound student. State and federal governments have worked together to make saving for college less onerous through the creation of tax-advantaged 529 plans. Analysis of the entire universe of 529Ps with 10 years of data reveals that a significant factor in differentiating between better and worse 529P investment performance is their expenses and their loads. Among the 529Ps with the lowest 25% of expenses, the authors find significantly better returns, lower standard deviation, and no appreciable impact on risk as measured by beta. Selection of 529Ps without loads also increases returns without increasing standard deviation or systematic risk. Risk-adjusted returns using standard deviation, negative return variance, or beta all demonstrate the value of avoiding high expenses and load/commission fees. Morningstar star ratings were also found to be higher, and downside capture ratios were found to be lower for the lower expense 529Ps.

Radiographic Assessment of Fibular Length Variance: The Case for “Fibula Minus”

Given the high prevalence of ankle fractures and morbidity of malalignment after fixation, an appropriate anatomic relationship between the distal fibula and adjacent tibia and talus is important. The tip of the lateral malleolus of the fibula has often been described to be at the level of the lateral talar process. However, no studies to date have examined the relationship of the distal fibular tip to the lateral process of the talus. We assessed 66 weightbearing mortise radiographs for variability of the distal fibular tip in relation to the lateral process of the talus. The subjects were all skeletally mature, with a mean age of 45.3 ± 14.6 years. We used a paired t test with a null hypothesis that the true mean difference in the distance from the distal fibula to the lateral process was equal to 0. The mean distance of the distal tip of the fibula was 0.257 ± 0.127 cm proximal to the tip of the lateral process of the talus. The 95% confidence interval was 0.226 to 0.288. Of the 66 subjects, 65 had the distal tip of the fibula proximal to the lateral process of the talus, corresponding to a negative fibular variance. In the remaining subject, the distal tip of the fibula was at the same level of the tip as the lateral process of the talus. The distal tip of the fibula is most commonly not at the level of the talus lateral process, as often described in published reports. Instead, it has a variance analogous to the relationship between the lengths of the ulna compared with the radius. The distal tip of the fibula in our study was more often proximal to the tip of the lateral process of the talus and can be described as a negative fibular variance, or “fibula minus.”

Activation, Avoidance, and Response-Contingent Positive Reinforcement Predict Subjective Wellbeing

We aimed to clarify whether relationships of behavioural activation, avoidance, and response-contingent positive reinforcement with subjective wellbeing components supported a behavioural subjective wellbeing model. We used a correlational, cross-sectional design, and collected data online from a convenience and snowball sample of 224 participants aged 18–72 years using an anonymous survey. We conducted hierarchical multiple regression analyses and structural equation modelling. Behavioural activation positively predicted significant unique variance in (a) positive affect, b = 0.44, 99.2% CI [0.30, 0.58], p < 0.001, $$\eta_{p}^{2}$$ = 0.27, and (b) satisfaction with life, b = 0.19, 99.2% CI [0.07, 0.31], p < 0.001, $$\eta_{p}^{2}$$ = 0.08. Behavioural activation negatively predicted significant unique negative affect variance, b = − 0.17, 99.2% CI [− 0.30, − 0.03], p = 0.001, $$\eta_{p}^{2}$$ = 0.05. Avoidance positively predicted significant negative affect variance, b = 0.45, 99.2% CI [0.31, 0.58], p < 0.001, $$\eta_{p}^{2}$$ = 0.27. Most other predictors had non-significant effects when behavioural variables were added to our models. Response-contingent positive reinforcement mediated relationships of behavioural activation with positive affect, negative affect, and satisfaction with life. Our results suggest behavioural variables may explain some affect variance often attributed to other predictors. The pattern of relationships we found supports a behavioural subjective wellbeing model focussed on response-contingent positive reinforcement. Our findings suggest these relationships may not be unique to depressed populations. Our results do not indicate causality, but suggest testing causality is warranted in the future.

Osteotomie du capitatum dans la maladie de Kienböck (à propos de 5 cas)

Kienbock's disease is a form of osteonecrosis affecting the lunate, which progresses through several stages if not treated. Usual surgical procedures unload the lunate. Radial shortening is the common procedure in negative ulnar variance. For wrists with neutral or positive ulnar variance, this procedure could produce a distal radio-ulnar discrepancy and an ulnocarpal impingement. We perform, in these cases, a capitate shortening. Our objective was to assess the long-term clinical and radiological outcome of capitate shortening for patients with neutral or positive ulnar variance at stage II and IIIA of disease. Between 2007 and 2013, 05 patients (03 male, 02 female) were operated. Their average age was 38 years (28 to 49). Two Lichtman's stage II and three stage III A with neutral or positive ulnar variance. The surgical procedure consisted in a dorsal approach and a 2 mm shortening osteotomy in the capitate's waist. Fixation was carried out by two memory staples. The mean follow-up was 03 years (29–60 months). Clinically, all patients had improved wrist pain, wrist range of movement, and grip strength after the surgery. Radiographic disease progression occurred only in 1 wrist, rated stage IIIB, at 4 years follow-up without a bad clinical outcome. The other 4 cases have all shown signs of revascularization. Any intracarpal complication or capitate non-union occurred. Capitate shortening osteotomy has been the subject of only a few publications. It is a simple and low aggressive procedure which is indicated in case of impossibility to achieve Radius shortening osteotomy in symptomatic patients. The osteotomy must be performed delicately, 2 mm shortening is sufficient to reduce 66 % of the pressure forces in the lunate lodge. However, it might be an efficient technique to induce the revascularization process in the early stages of Kienbock's disease. The capitate shortening is a simple and low aggressive procedure. We recommend this procedure for symptomatic patients in early Kienbock's disease with neutral or positive ulnar variance.

Genome-wide analysis of the diversity and ancestry of Korean dogs.

There are various hypotheses on dog domestication based on archeological and genetic studies. Although many studies have been conducted on the origin of dogs, the existing literature about the ancestry, diversity, and population structure of Korean dogs is sparse. Therefore, this study is focused on the origin, diversity and population structure of Korean dogs. The study sample comprised four major categories, including non-dogs (coyotes and wolves), ancient, modern and Korean dogs. Selected samples were genotyped using an Illumina CanineHD array containing 173,662 single nucleotide polymorphisms. The genome-wide data were filtered using quality control parameters in PLINK 1.9. Only autosomal chromosomes were used for further analysis. The negative off-diagonal variance of the genetic relationship matrix analysis depicted, the variability of samples in each population. FIS (inbreeding rate within a population) values indicated, a low level of inbreeding within populations, and the patterns were in concordance with the results of Nei’s genetic distance analysis. The lowest FST (inbreeding rate between populations) values among Korean and Chinese breeds, using a phylogenetic tree, multi-dimensional scaling, and a TreeMix likelihood tree showed Korean breeds are highly related to Chinese breeds. The Korean breeds possessed a unique and large diversity of admixtures compared with other breeds. The highest and lowest effective population sizes were observed in Korean Jindo Black (485) and Korean Donggyeong White (109), respectively. The historical effective population size of all Korean dogs showed declining trend from the past to present. It is important to take immediate action to protect the Korean dog population while conserving their diversity. Furthermore, this study suggests that Korean dogs have unique diversity and are one of the basal lineages of East Asian dogs, originating from China.

Bayesian analysis of homogeneity studies in the production of reference materials

For almost two decades, the batch homogeneity in the production of reference materials has been evaluated using analysis of variance (ANOVA) to determine the between-bottle standard deviation. This approach replaced at that time the use of the F-test in ANOVA to determine whether the ratio of the mean squares $${MS}_{\mathrm {between}}/{MS}_{\mathrm {within}}$$ is statistically significant. Problems arise when $${MS}_{\mathrm {between}} < {MS}_{\mathrm {within}}$$ , because classical ANOVA provides a negative between-bottle variance, which is then often set to zero. By using a Bayesian hierarchical model, based on the same assumptions as traditional ANOVA, we show that even if $${MS}_{\mathrm {between}} < {MS}_{\mathrm {within}}$$ , there can be a relevant level of between-bottle inhomogeneity to account for. The Bayesian analysis produces a nonzero value for the between-bottle standard deviation, which dismisses the practice of setting this standard deviation to 0. At the same time, it dismisses the current guidance given in ISO Guide 35 under these circumstances. Finally, it is shown that traditional ANOVA, meta-analysis methods and Bayesian analysis give very similar answers as long as $${MS}_{\mathrm {between}} > {MS}_{\mathrm {within}}$$ , so there is no need to discourage using these methods in favour of a Bayesian analysis, provided that the repeatability of the measurement method used to conduct the between-bottle homogeneity study is sufficient to characterise the dispersion across the bottles (items) in a batch of a reference material.

A Production Economy with Shocks in the Volatility of Capital Stock and Its Application

This paper extends the AK production model in Pindyck and Wang (2013) into a more general setting in which the volatility of capital stock is stochastic and driven by shocks. After solving the equilibrium, the fundamental shocks are embedded into the stock price and the leverage effect is contributed from three distinct channels. As two applications, we employ our extended AK production model to match well the negative variance risk premium and the Black-Scholes implied volatility surface.

The Cross-Sectional Variation of Skew Risk Premia

This paper estimates the variance risk premium (VRP) and the skew risk premium (SRP) for the individual stocks and indexes in the US financial markets, and then further analyzes the determinants of the cross-sectional variations of (i) the VRP and SRP for 40 indexes and stocks, (ii) the average VRP and SRP for a representative set of portfolios sorted by the VRP and SRP betas, and (iii) the future variance and skew risk premia. We find that (i) most of the stocks and indexes have negative and significant variance and skew risk premia; (ii) the default premium (DEF) is the key risk factor in the cross-sectional variation of average SRP payoffs; and (iii) there is a negative and statistically significant relation between the future SRP and the lottery demand factor (MAX).

Risk Factors of Lunate Collapse in Kienböck Disease

Not all patients with Kienböck disease progress to collapse of the lunate and carpal malalignment, but it is difficult to determine which patients are at risk. We aimed to identify demographic or anatomical factors associated with more advanced stages of Kienböck disease. We included all 195 eligible patients with Kienböck disease and available preoperative posteroanterior and lateral radiographs. We compared the mean age, sex distribution, mean ulnar variance, radial height, radial (ulnarward) inclination, palmar tilt, anteroposterior distance, and lunate type among the different Lichtman stages of Kienböck disease and performed ordinal logistic regression analysis. We found that patients with more negative ulnar variance had more advanced stages of Kienböck disease (adjusted odds ratio, 1.4). An increase in age was also independently associated with a higher Lichtman stage of Kienböck disease (adjusted odds ratio, 1.02). Our findings suggest that more negative ulnar variance may be related to a greater magnitude of lunate collapse in Kienböck disease. Additional long-term study is needed to confirm the longitudinal relationship of negative ulnar variance with progressive Kienböck disease. Prognostic II.

Negative Dependence, Scrambled Nets, and Variance Bounds

In this paper, we provide a framework to study the dependence structure of sampling schemes such as those produced by randomized quasi-Monte Carlo methods. The main goal of this new framework is to determine conditions under which the negative dependence structure of a sampling scheme enables the construction of estimators with reduced variance compared to Monte Carlo estimators. To do this, we establish a generalization of the well-known Hoeffding’s lemma—expressing the covariance of two random variables as an integral of the difference between their joint distribution function and the product of their marginal distribution functions—that is particularly well suited to study such sampling schemes. We also provide explicit formulas for the joint distribution of pairs of points randomly chosen from a scrambled (0, m, s)-net. In addition, we provide variance bounds establishing the superiority of dependent sampling schemes over Monte Carlo in a few different setups. In particular, we show that a scrambled (0, m, 2)-net yields an estimator with variance no larger than a Monte Carlo estimator for functions monotone in each variable.

Will the Real Factors of Prosociality Please Stand Up? A Comment on Böckler, Tusche, and Singer (2016)

Based on a large individual differences study, Böckler, Tusche, and Singer aimed to develop and psychometrically evaluate measurement procedures that capture individual differences in multiple facets of human prosociality. Böckler et al. claimed that they identified four reliable and method-independent subcomponents of human prosociality: altruistically motivated prosocial behavior, norm-motivated prosocial behavior, strategically motivated prosocial behavior, and self-reported prosocial behavior. We show that this claim is not supported by the data. The abnormalities of the factor solution are visible in reported standardized loadings much larger than unity and negative residual variances at the indicator level. Additionally, the strong dispersion in factor loadings reported in the article hinders factor interpretation. We reanalyze the correlation matrices and propose a model with one overarching prosociality factor and a specific factor for game-theoretical conflicts. This simpler model is a more sustainable representation of prosocial behavior.

Forecast evaluation tests and negative long-run variance estimates in small samples

This paper shows that the long-run variance can frequently be negative when computing standard Diebold–Mariano-type tests for equal forecast accuracy and forecast encompassing if one is dealing with multi-step-ahead predictions in small, but empirically relevant, sample sizes. We therefore consider a number of alternative approaches for dealing with this problem, including direct inference in the problem cases and the use of long-run variance estimators that guarantee positivity. The finite sample size and power of the different approaches are evaluated using extensive Monte Carlo simulation exercises. Overall, for multi-step-ahead forecasts, we find that the test recently proposed by Coroneo and Iacone (2016), which is based on a weighted periodogram long-run variance estimator, offers the best finite sample size and power performance.

The effect of different data structures, sample sizes on model fit measures

ABSTRACTIn this study, our aim was to investigate the changes of different data structures and different sample sizes on the structural equation modeling and the influence of these factors on the model fit measures. Examining the created structural equation modeling under different data structures and sample sizes, the evaluation of model fit measures were performed with a simulation study. As a result of the simulation study, optimization and negative variance estimation problems have been encountered depending on the sample size and changing correlations. It was observed that these problems disappeared either by increasing the sample size or the correlations between the variables in factor. For upcoming studies, the choice of RMSEA and IFI model fit measures can be suggested in all sample sizes and the correlation values for data sets are ensured the multivariate normal distribution assumption.

Robust inversion method for jointly estimating parameters and variance components from heterogeneous monitoring data

In this paper, a novel inversion method is proposed for jointly robust estimation of parameters and variance components from disjunctive groups of observations affected by outliers. This method, named robust non-negative variance component estimation (RVCE), is a coupling of variance component estimation (VCE) technique with a robust estimation method, developed to cope with outliers and to avoid negative variance, leading thus, to an estimation reliable enough. The principle of RVCE method is based on the refinement of the stochastic model via an equivalent weight matrix established from the original measurement weight matrix and an adapted full weight matrix with hard rejection to outliers. This last one is derived from the robust standardized residuals, using a highly robust estimator, as an initial solution of the inverse problem, and a cut-off value adapted to sort out the good observations from the bad ones. Furthermore, because the original weight matrix is partly known, the integration of the VCE technique plays a key role to reach an optimal solution and to provide valuable information on the precision of the estimates. The performance of the proposed method is demonstrated by considering a rockfill dam as an example, where the material parameters and variance components are jointly estimated from geotechnical and geodetic measurements. The results of comparison study between RVCE method with other methods such as the classical NN-VCE, RIMCO, least squares and the combined Huber’s M-estimator with VCE (HVCE) for various configuration options have highlighted the pertinence of the proposed method.

Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance

In chronic psychotic patients, word graph analysis shows potential as complementary psychiatric assessment. This analysis relies mostly on connectedness, a structural feature of speech that is anti-correlated with negative symptoms. Here we aimed to verify whether speech disorganization during the first clinical contact, as measured by graph connectedness, can correctly classify negative symptoms and the schizophrenia diagnosis 6 months in advance. Positive and negative syndrome scale scores and memory reports were collected from 21 patients undergoing first clinical contact for recent-onset psychosis, followed for 6 months to establish diagnosis, and compared to 21 well-matched healthy subjects. Each report was represented as a word-trajectory graph. Connectedness was measured by number of edges, number of nodes in the largest connected component and number of nodes in the largest strongly connected component. Similarities to random graphs were estimated. All connectedness attributes were combined into a single Disorganization Index weighted by the correlation with the positive and negative syndrome scale negative subscale, and used for classifications. Random-like connectedness was more prevalent among schizophrenia patients (64 × 5% in Control group, p = 0.0002). Connectedness from two kinds of memory reports (dream and negative image) explained 88% of negative symptoms variance (p < 0.0001). The Disorganization Index classified low vs. high severity of negative symptoms with 100% accuracy (area under the receiver operating characteristic curve = 1), and schizophrenia diagnosis with 91.67% accuracy (area under the receiver operating characteristic curve = 0.85). The index was validated in an independent cohort of chronic psychotic patients and controls (N = 60) (85% accuracy). Thus, speech disorganization during the first clinical contact correlates tightly with negative symptoms, and is quite discriminative of the schizophrenia diagnosis.

Structural validity of the Wechsler Intelligence Scale for Children-Fifth Edition: Confirmatory factor analyses with the 16 primary and secondary subtests.

The factor structure of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V; Wechsler, 2014a) standardization sample (N = 2,200) was examined using confirmatory factor analyses (CFA) with maximum likelihood estimation for all reported models from the WISC-V Technical and Interpretation Manual (Wechsler, 2014b). Additionally, alternative bifactor models were examined and variance estimates and model-based reliability estimates (ω coefficients) were provided. Results from analyses of the 16 primary and secondary WISC-V subtests found that all higher-order CFA models with 5 group factors (VC, VS, FR, WM, and PS) produced model specification errors where the Fluid Reasoning factor produced negative variance and were thus judged inadequate. Of the 16 models tested, the bifactor model containing 4 group factors (VC, PR, WM, and PS) produced the best fit. Results from analyses of the 10 primary WISC-V subtests also found the bifactor model with 4 group factors (VC, PR, WM, and PS) produced the best fit. Variance estimates from both 16 and 10 subtest based bifactor models found dominance of general intelligence (g) in accounting for subtest variance (except for PS subtests) and large ω-hierarchical coefficients supporting general intelligence interpretation. The small portions of variance uniquely captured by the 4 group factors and low ω-hierarchical subscale coefficients likely render the group factors of questionable interpretive value independent of g (except perhaps for PS). Present CFA results confirm the EFA results reported by Canivez, Watkins, and Dombrowski (2015); Dombrowski, Canivez, Watkins, and Beaujean (2015); and Canivez, Dombrowski, and Watkins (2015). (PsycINFO Database Record