D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [26, 27, 30, 34, 36, 38, 43, 45]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 13, 28, 44] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [23, 24, 27, 31, 33, 35, 41]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 12, 25, 40] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [24, 25, 28, 32, 34, 36, 41, 43]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 12, 26, 42] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.

....and maxima. The search for a minimum cost state sequence in the automata of Section 2 and 4 can also be viewed as a search for approximate level sets in a time series, and hence related to the large body of work on piece wise function approximation in both statistics and data mining (see e.g. [23, 24, 27, 31, 33, 35, 41]) In a discrete framework, work on mining episodes and sequential patterns (e.g. 1, 12, 25, 40] has developed algorithms to identify particular configurations of discrete events clustered in time, in some cases obeying partial precedence constraints on their order. Finally, there is an ....

D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.

....is a usual generalized linear model, if the breakpoints i are known. Therefore the ML estimation can be performed by a grid search type algorithm in case of two segments, see Stasinopoulos and Rigby (1992) For the linear model an exact algorithm for the least squares estimator was given by Hudson (1966) (see also Schulze, 1987 or Hawkins, 1976) In Section 2 it is shown, that this algorithm also works for the GLM with one breakpoint. In Section 3 the algorithm is extended to models with further covariates. In Section 4 the ideas of Section 3 are used to derive the algorithm for fairly general ....

....Ulm (1991) For a GLIM macro see Stasinopoulos and Rigby (1992) Though these algorithms give reliable results, if the grid is appropriately chosen, it would be desirable to have an exact algorithm at one s disposal. 4 We derive such an exact algorithm for maximizing (5) following the ideas of Hudson (1966). Since we have assumed, that the nuisance parameter is constant for the two segments, it can be neglected in maximizing (5) Let the observations (x i ; y i ) i=1; n be ordered with respect to x i such that x i x j for i j. The log likelihood is differentiable with respect to = ff; ....

Hudson, D. J. (1966). Fitting segmented curves whose join points have to be estimated, Journal of the American Statistical Association 61: 1097-- 1129.

....likelihood surfaces that can result in multiple change point settings. Hinkley (1969) detailed maximum likelihood estimation and asymptotic confidence regions for the single change point model with the continuity constraint. One of the rare early cases where multiple changepoints were permitted is Hudson (1966), which describes algorithms for obtaining least squares estimates of piecewise linear curves when the change point lies strictly between two data x values or when the changepoint exactly equals a data x value. Bayesian approaches to the problem have been considered ever since Bacon and Watts ....

....of homicides and suicides by explosives, we do not view this classification as a major problem. We have access to the Mortality Detail Files from 1968 through 1993; this represents the addition of two years to the data series used in Cork (1996) 4.5. Preliminary Results. In Cork (1996) we used Hudson s (1966) suggestions for obtaining maximum likelihood estimates of regression change points in analyzing trends in gun related homicide, suicide, and accidents. Up to two change points were permitted in fitting a split line trend to the data, imposing the constraint that the fitted curves be continuous at ....

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Hudson, D. J. (1966). "Fitting Segmented Curves Whose Join Points Have to be Estimated." Journal of the American Statistical Association, 1097--1129.

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D. Hudson, "Fitting segmented curves whose join points have to be estimated," Journal of the American Statistical Association 61(1966) pp. 1097--1129.

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