Robust regression in biological assay: application to the evaluation of alternative experimental techniques.

Robust Huber type regression and testing of linear hypotheses are adapted to statistical analysis of parallel line and slope ratio assays. They are applied in the evaluation of results of several experiments carried out in order to compare and validate alternatives to animal experimentation based on embryo and cell cultures. Computational procedures necessary for the application of robust methods of analysis used the conversational statistical package ROBSYS. Special commands for the analysis of parallel line and slope ratio assays have been added to ROBSYS.

Assessing relationships between genetic evaluations using robust regression with an application to holsteins in Uruguay

Globalization of dairy cattle breeding has created a need for international sire proofs. Some early methods for converting proofs from one population to another are based on simple linear regression. An alternative robust regression method based on the t-distribution is presented, and maximum likelihood and Bayesian techniques for analysis are described, including the situation in which some proofs are missing. Procedures were used to investigate the relationship between Holstein sire proofs obtained by two Uruguayan genetic evaluation programs. The results suggest that conversion equations developed from data including only sires having proofs in both populations can lead to distorted results, relative to estimates obtained using techniques for incomplete data. There was evidence of non-normality of regression residuals, which constitutes an additional source of bias. A robust estimator may not solve all problems, but can provide simple conversion equations that are less sensitive to outlying proofs and to departures from assumptions.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

Robust accelerated failure time regression

Robust estimators for accelerated failure time models with asymmetric (or symmetric) error distribution and censored observations are proposed. It is assumed that the error model belongs to a log-location-scale family of distributions and that the mean response is the parameter of interest. Since scale is a main component of mean, scale is not treated as a nuisance parameter. A three steps procedure is proposed. In the first step, an initial high breakdown point S estimate is computed. In the second step, observations that are unlikely under the estimated model are rejected or down weighted. Finally, a weighted maximum likelihood estimate is computed. To define the estimates, functions of censored residuals are replaced by their estimated conditional expectation given that the response is larger than the observed censored value. The rejection rule in the second step is based on an adaptive cut-off that, asymptotically, does not reject any observation when the data are generat ed according to the model. Therefore, the final estimate attains full efficiency at the model, with respect to the maximum likelihood estimate, while maintaining the breakdown point of the initial estimator. Asymptotic results are provided. The new procedure is evaluated with the help of Monte Carlo simulations. Two examples with real data are discussed.

Tests of sunspot number sequences: 3. Effects of regression procedures on the calibration of historic sunspot data

We use sunspot group observations from the Royal Greenwich Observatory (RGO) to investigate the effects of intercalibrating data from observers with different visual acuities. The tests are made by counting the number of groups RB above a variable cut-off threshold of observed total whole-spot area (uncorrected for foreshortening) to simulate what a lower acuity observer would have seen. The synthesised annual means of RB are then re-scaled to the full observed RGO group number RA using a variety of regression techniques. It is found that a very high correlation between RA and RB (rAB > 0.98) does not prevent large errors in the intercalibration (for example sunspot maximum values can be over 30 % too large even for such levels of rAB). In generating the backbone sunspot number (RBB), Svalgaard and Schatten (2015, this issue) force regression fits to pass through the scatter plot origin which generates unreliable fits (the residuals do not form a normal distribution) and causes sunspot cycle amplitudes to be exaggerated in the intercalibrated data. It is demonstrated that the use of Quantile-Quantile (“Q  Q”) plots to test for a normal distribution is a useful indicator of erroneous and misleading regression fits. Ordinary least squares linear fits, not forced to pass through the origin, are sometimes reliable (although the optimum method used is shown to be different when matching peak and average sunspot group numbers). However, other fits are only reliable if non-linear regression is used. From these results it is entirely possible that the inflation of solar cycle amplitudes in the backbone group sunspot number as one goes back in time, relative to related solar-terrestrial parameters, is entirely caused by the use of inappropriate and non-robust regression techniques to calibrate the sunspot data.

Dimension Reduction of the Explanatory Variables in Multiple Linear Regression

2002 Mathematics Subject Classification: 62J05, 62G35.

Ciclo de vida empresarial e qualidade da informação contábil das companhias abertas brasileiras

Esta dissertação busca identificar se os diferentes Estágios de Ciclo de Vida (ECVs) estão relacionados com a qualidade da informação contábil nas empresas brasileiras. Segundo pesquisas internacionais, os diferentes ECVs influenciam a qualidade da informação contábil. Aqui, foram empregadas as métricas de relevância, tempestividade e conservadorismo, de maneira semelhante às utilizadas por Lopes (2009) para verificar a qualidade da informação contábil. Para identificar os Estágios de Ciclo de Vida, foi utilizada a forma de identificação orgânica elaborada por Dickinson (2011), fundamentada nos sinais dos fluxos de caixa da empresa. A amostra deste trabalho é composta por empresas brasileiras que negociaram ações na BM&FBovespa, no período de 2008 à 2013, sendo excluídas as empresas do setor financeiro. O total de empresas que compõem a amostra é de 330, sendo 1.163 observações para o modelo de relevância, 1.163 para o modelo de tempestividade e 1.167 observações para o modelo de conservadorismo. Para verificar os efeitos dos ECVs na qualidade da informação contábil foram utilizados dados em painel desbalanceado e regressões robustas, com a correção de White, identificando os ECVs através de dummies. Os resultados encontrados indicam que os ECVs afetam a qualidade da informação contábil e que nos estágios de Crescimento e Maturidade as informações contábeis apresentam maior relevância e tempestividade. Não foi possível identificar os efeitos dos diferentes ECVs no conservadorismo, pois as variáveis de interesse não foram estatisticamente significantes.

Does history of falling predict length of stay in post-scute geriatric rehabilitation?

Objectives: To investigate the associations between falls before¦hospital admission, falls during hospitalization, and length of stay in¦elderly people admitted to post-acute geriatric rehabilitation.¦Method: History of falling in the previous 12 months before admission¦was recorded among 249 older persons (mean age 82.3 ± 7.4 years,¦69.1% women) consecutively admitted to post-acute rehabilitation. Data¦on medical, functional and cognitive status were collected upon¦admission. Falls during hospitalization and length of stay were recorded¦at discharge.¦Results: Overall, 92 (40.4%) patients reported no fall in the 12 months¦before admission; 63(27.6%) reported 1 fall, and 73 (32.0%) reported¦multiple falls. Previous falls occurrence (one or more falls) was¦significantly associated with in-stay falls (19.9% of previous fallers fell¦during the stay vs 7.6% in patients without history of falling, P = .01),¦and with a longer length of stay (22.4 ± 10.1 days vs 27.1 ± 14.3 days,¦P = .01). In multivariate robust regression controlling for gender, age,¦functional and cognitive status, history of falling remained significantly¦associated with longer rehabilitation stay (2.8 days more than non¦fallers in single fallers, p = .05, and 3.3 days in multiple fallers, p = .0.1).¦Conclusion: History of falling in the 12 months prior to post acute¦geriatric rehabilitation is independently associated with a longer¦rehabilitation length of stay. Previous fallers also have an increased risk¦of falling during rehabilitation stay. This suggests that hospital fall¦prevention measures should particularly target these high risk patients.

Does history of falling predict length of stay in post-acute geriatric rehabilitation?

Objectives:To investigate the associations between falls before hospital¦admission, falls during hospitalization, and length of stay in elderly¦people admitted to post-acute geriatric rehabilitation. Method: History¦of falling in the previous 12 months before admission was recorded¦among 249 older persons (mean age 82.3±7.4 years, 69.1% women)¦consecutively admitted to post-acute rehabilitation. Data on medical,¦functional and cognitive status were collected upon admission. Falls¦during hospitalization and length of stay were recorded at discharge.¦Results: Overall, 92 (40.4%) patients reported no fall in the 12 months¦before admission; 63(27.6%) reported 1 fall, and 73(32.0%) reported¦multiple falls. Previous falls occurrence (one or more falls) was significantly¦associated with in-stay falls (19.9% of previous fallers fell¦during the stay vs 7.6% in patients without history of falling, P=.01),¦and with a longer length of stay (22.4 ± 10.1 days vs 27.1 ± 14.3 days,¦P=.01). In multivariate robust regression controlling for gender, age,¦functional and cognitive status, history of falling remained significantly¦associated with longer rehabilitation stay (2.8 days more in single fallers,¦p=.05, and 3.3 days more in multiple fallers, p=.0.1, compared to¦non-fallers). Conclusion: History of falling in the 12 months prior to¦post acute geriatric rehabilitation is independently associated with a¦longer rehabilitation length of stay. Previous fallers have also an¦increased risk of falling during rehabilitation stay. This suggests that¦hospital fall prevention measures should particularly target these high¦riskpatients.

Red cell distribution width does not predict stroke severity or functional outcome.

INTRODUCTION: Red cell distribution width was recently identified as a predictor of cardiovascular and all-cause mortality in patients with previous stroke. Red cell distribution width is also higher in patients with stroke compared with those without. However, there are no data on the association of red cell distribution width, assessed during the acute phase of ischemic stroke, with stroke severity and functional outcome. In the present study, we sought to investigate this relationship and ascertain the main determinants of red cell distribution width in this population. METHODS: We used data from the Acute Stroke Registry and Analysis of Lausanne for patients between January 2003 and December 2008. Red cell distribution width was generated at admission by the Sysmex XE-2100 automated cell counter from ethylene diamine tetraacetic acid blood samples stored at room temperature until measurement. An χ(2) -test was performed to compare frequencies of categorical variables between different red cell distribution width quartiles, and one-way analysis of variance for continuous variables. The effect of red cell distribution width on severity and functional outcome was investigated in univariate and multivariate robust regression analysis. Level of significance was set at 95%. RESULTS: There were 1504 patients (72±15·76 years, 43·9% females) included in the analysis. Red cell distribution width was significantly associated to NIHSS (β-value=0·24, P=0·01) and functional outcome (odds ratio=10·73 for poor outcome, P<0·001) at univariate analysis but not multivariate. Prehospital Rankin score (β=0·19, P<0·001), serum creatinine (β=0·008, P<0·001), hemoglobin (β=-0·009, P<0·001), mean platelet volume (β=0·09, P<0·05), age (β=0·02, P<0·001), low ejection fraction (β=0·66, P<0·001) and antihypertensive treatment (β=0·32, P<0·001) were independent determinants of red cell distribution width. CONCLUSIONS: Red cell distribution width, assessed during the early phase of acute ischemic stroke, does not predict severity or functional outcome.