Your Death Would Be Mine Written By Martha Hanna Tells

Your Death Would Be Mine written by Martha Hanna tells the story of life in France during World War I through the intimate correspondences of Marie and Paul Pireaud. Marie and Paul were newlywed’s who wrote daily letter correspondences during World War I. Paul was a French soldier who spent the entire duration of the First Great War, from 1914-1918, on the front lines. Marie was on the home front working the land and pregnant with the couples first child, who was born through a difficult childbirth. The Pireaud’s were two peasants from rural France in the Dordogne region. Hanna uses over two thousand correspondences between Marie and Paul to illustrate what life was like. Marie and Paul’s letters covered a wide variety of topics such as:†¦show more content†¦Paul’s experience of the Great War was not identical in all details to that of every other French soldier mobilized in defense of the nation; Marie’s was not that of every young woma n waiting at home. But theirs is a story– rich in detail, passionate in its expression–that is worth retelling. Hanna consistently maintained the thesis through out the book because the letters allowed readers to see what it was like to live as a peasant in rural France during War World I. Hanna consistently shows why the Pireaud’s story should be told. Hanna used the letters to explain different things that were happening militarily; Hanna described Paul’s experiences in some of France’s bloodiest battle, such as the Verdun and the Somme. Hanna’s use of Paul’s description of the battles is one of the many examples of how the thesis was maintained through out the book; the examples keep with the part of Hanna’s thesis that mentioned how terrifying the First Great was. Hanna also provided readers with historical context, which supported the thesis because it allowed readers to see how the war came

The interviews addressed buying attitudes Free Essays

OBJECTIVE: Compulsive buying (uncontrolled urges to buy, with resulting significant adverse consequences) has been estimated to affect from 1. 8% to 16% of the adult U. S. We will write a custom essay sample on The interviews addressed buying attitudes or any similar topic only for you Order Now population. To the authors’ knowledge, no study has used a large general population sample to estimate its prevalence. METHOD: The authors conducted a random sample, national household telephone survey in the spring and summer of 2004 and interviewed 2,513 adults. The interviews addressed buying attitudes and behaviors, their consequences, and the respondents’ financial and demographic data. The authors used a clinically validated screening instrument, the Compulsive Buying Scale, to classify respondents as either compulsive buyers or not. RESULTS: The rate of response was 56. 3%, which compares favorably with rates in federal national health surveys. The cooperation rate was 97. 6%. Respondents included a higher percentage of women and people ages 55 and older than the U. S. adult population. The estimated point prevalence of compulsive buying among respondents was 5. 8% (by gender: 6. 0% for women, 5. 5% for men). The gender-adjusted prevalence rate was 5. 8%. Compared with other respondents, compulsive buyers were younger, and a greater proportion reported incomes under $50,000. They exhibited more maladaptive responses on most consumer behavior measures and were more than four times less likely to pay off credit card balances in full. CONCLUSIONS: A study using clinically valid interviews is needed to evaluate these results. The emotional and functional toll of compulsive buying and the frequency of comorbid psychiatric disorders suggest that studies of treatments and social interventions are warranted Source: American Journal of Psychiatry: http://ajp. psychiatryonline. org/cgi/content/abstract/163/10/1806 How to cite The interviews addressed buying attitudes, Papers

Equations in Nonparametric Instrumental †MyAssignmenthelp.com

Question: Discuss about the Equations in Nonparametric Instrumental Regression. Answer: Introduction: The topic selected for the critical analysis of the article is Nonparametric density and regression estimation. From the article, it has been analysed that nonparametric density are used to specify the models but it is very difficult to compare these densities with the parametric densities in the model specifications. The nonparametric densities converge slower as depends hugely on increased variables and different dimensions in order to evaluate the accurate results (Lei and Wasserman, 2014). The regression estimation requires more variables to specify densities on the curve to carve out more positive results. From the theoretical results, it has been analysed that if nonparametric density satisfies mild assumption of differential densities then convergence rate of the design curve will solely determined with the smoothness of density curve and coefficient of density for the curve. For the regression estimation, fixed design concept has been adopted. The regression estimation will not create any impact on the density curve for the nonparametric variables. The lower bound, upper bound or minimum value on the density curve will not be affected to different in design of the density curve during the regression analysis (Dunker et al, 2014). The methodology adopted for regression estimation is mainly used for classical models like time series model. The main problem associated with this methodology is that observations are not independent and covariate determination also becomes difficult to evaluate during the regression analysis. The nonparametric density estimation has drawbacks like density estimation will require more parameters to evaluate more efficient results. The summarization of the estimates is also difficult as requires entire information which is contained during the density estimation. The nonparametric modelling prefers over parametric modelling due to its flexibility as provide choices from infinite dimensional variables to define the functional relation under the regression curve (Veraverbeke et al, 2014). The choice of parameters is entirely depends on smoothness associated with the density curves. But in most cases, one can make assumption of mild restrictions according to which the regression curves have first derivative in continuous manner and second derivative in the square integrated form. The errors in mean squares of nonparametric estimators are having the rate of n , [0, 1] where value of is entirely depends on underlying curve's smoothness. The functional procedures adopted for a daptive basics in nonparametric models have ability to define curvature for different functions on different locations along the curve. The approach of sequential can also be sued to estimate the regression function for the different dependent observations on the density curves. The estimation mainly includes input process and holder class estimates in order to evaluate the positive probability for the inbounded density curves. A sequential estimator generally provides accurate mean squared values on finite curves (Menardi and Azzalini, 2014). However, truncated estimators generally have finite sample size and variance is also known to determine the actual value for the regression coefficient. The various techniques like Silverman's rule-of-thumb, bootstrapping, pilot methods and many more are developed for the bandwidth selectors. These techniques are simple to visualise and describe the data in order to evaluate the desired inferences. Hence, it is evident that various econometricians have studied the density estimation in terms of both parametric as well as nonparametric approaches for identifying the appropriate structure and from that to make inferences related to the true models about the density and regression analysis. The kernel smoothing model can be used for further research as provides more flexibility in estimating the density and regression. This model can easily estimate probability for the density function using the random variables. This technique is effective to describe and represent the data on the models and accordingly make inferences from the model to estimate the desired results efficiently. References: Dunker, F., Florens, J., Hohage, T., Johannes, J. and Mammen, E. (2014). Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression. Journal of Econometrics, 178, 444-455. Lei, J. and Wasserman, L. (2014). Distribution?free prediction bands for non?parametric regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1), 71-96. Menardi, G. and Azzalini, A. (2014). An advancement in clustering via nonparametric density estimation. Statistics and Computing, 24(5), 753-767. Veraverbeke, N., Gijbels, I. and Omelka, M. (2014). Preadjusted non?parametric estimation of a conditional distribution function. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 399-438.