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Roberts, G. O. and Polson, N. G. (1994). On the Geometric Convergence of the Gibbs Sampler. Journal of the Royal Statistical Society, Series B 56, 377 84. Roberts, G. O. and Smith, A. F. M. (1994). Simple Conditions for the Convergence of the Gibbs Sampler and Metropolis Hastings Algorithms. Stochastic Processes and Their Applications 44, 207 16. Robinson, W. S. (1950). Ecological Correlation and the Behavior of Individuals. American Sociological Review 15, 351 57. Rogers, J., Filliben, J., Gill, L., Guthrie, W., Lagergren, E., and Vangel, M. (2000). StRD: Statistical Reference Datasets for Testing the Numerical Accuracy of Statistical Software. NIST 1396. Washington, DC: National Institute of Standards and Technology. Rosenthal, J. S. (1993). Rates of Convergence for Data Augmentation on Finite Sample Spaces. Annals of Applied Probability 3, 819 39. Rosenthal, J. S. (1995a). Rates of Convergence for Gibbs Sampling for Variance Component Models. Annals of Statistics 23, 740 61. Rosenthal, J. S. (1995b). Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo. Journal of the American Statistical Association 90, 558 66. Ross, S. (1996). Stochastic Processes. New York: John Wiley & Sons. Rotkiewicz, A. (1972). W. Sierpinski s Works on the theory of numbers. Rend. Circ. Mat. Palermo 21, 5 24. Rousseeuw, P. J. and Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: John Wiley & Sons. Rubin, D. B. (1981). The Bayesian Bootstrap. Annals of Statistics 9, 130 34.



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where y and x denote the average of y and x, respectively. The numerator of b is also known as the covariance between x and y (multiplied by n), and the denominator is identical to the variance of x (multiplied by n). The presence of the covariance is intuitive since b estimates the amount of change in y for a change in x. Dividing by the variance of x discounts this movement of x by its volatility, since high volatility independent variables may move a large distance for small changes in y. These are the estimated values of the OLS parameters that give the lowest sum of squared prediction errors. They do not give a precise value, however. One can think of the estimates as being in a range around the true value. Given a large enough sample size (over 30 observations) this range can be determined by the normal distribution. Given that the OLS assumptions hold, the estimates a and b in (1.A.5) are unbiased, meaning that on average they fall around the true parameter value, and they are the best estimates in that they have the lowest variance around

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(11.41) From (11.41) we notice that the vector [Fd ] on the left-hand side is represented as a linear combination of N independent vectors, which, for the brevity of presentation, we name [E1 ], [E2 En ], respectively. For solutions of unknown terms A1 , AN , a necessary condition is that [Fd ] must lie on the space spanned by the A2 vectors [E1 ], [E2 En ]. Otherwise an exact solution is not possible. For M > N , it is quite possible that [Fd ] lies outside the space of [En ] s. This is pictorially shown in Figure 11.16 for N = 2 and M = 3. The best possible solution in the least squares

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{Dynamic Break Out II by George Pruitt This system is an extension of the original Dynamic Break Out system written by George for Futures Magazine in 1996. In addition to the channel break out methodology, DBS II incorporates Bollinger Bands to determine trade entry.} Inputs: ceilingAmt(60),floorAmt(20),bolBandTrig(2.00); Vars: lookBackDays(20),todayVolatility(0),yesterDayVolatility(0), deltaVolatility(0); Vars: buyPoint(0),sellPoint(0),longLiqPoint(0),shortLiqPoint(0),upBand(0), dnBand(0); todayVolatility = StandardDev(Close,30,1); yesterDayVolatility = StandardDev(Close[1],30,1); {See how I offset the function call to get yesterday's value} deltaVolatility = (todayVolatility - yesterDayVolatility)/todayVolatility; lookBackDays = lookBackDays * (1 + deltaVolatility); lookBackDays = Round(lookBackDays,0); lookBackDays = MinList(lookBackDays,ceilingAmt); {Keep adaptive engine within bounds} lookBackDays = MaxList(lookBackDays,floorAmt); upBand = BollingerBand(Close,lookBackDays,+bolBandTrig); dnBand = BollingerBand(Close,lookBackDays,-bolBandTrig); buyPoint = Highest(High,lookBackDays); sellPoint = Lowest(Low,lookBackDays); longLiqPoint = Average(Close,lookBackDays); shortLiqPoint = Average(Close,lookBackDays); if(Close > upBand) then Buy("DBS-2 Buy") tomorrow at buyPoint stop; if(Close < dnBand) then SellShort("DBS-2 Sell") tomorrow at sellPoint stop;

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