adaptive fractionally spaced blind cma equalization excess mse the performance of the constant modulus algorithm cma a reference algorithm for adaptive blind equalization is studied in terms of the excess mean square error emse due to the nonvanishing step size of the gradient descent algorithm an analytical approximation of emse is provided emphasizing the effect of the constellation size and resulting in design guidelines

a new approach to multipath correction of constant modulus signals an adaptive digital filtering algorithm that can compensate for both frequency selective multipath and interference on constant envelope modulated signals is presented the method exploits the fact that multipath reception and various interference sources generate incidental amplitude modulation on the received signal a class of so called constant modulus performance functions is developed which sense this am term but are insensitive to the angle modulation simple adaptive algorithms for finite impulse response fir digital filters are developed which employ a gradient search of the performance function one of the resulting algorithms is simulated for the example of an fm signal degraded by specular multipath propagation substantial improvements in noise power ratio npr are observed e g 25 db with moderately rapid convergence time these results are then extended to include tonal interference on a fm signal and intersymbol interference on a qpsk data signal

the gaussian assumption in second order estimation problems in digital communications this paper deals with the goodness of the gaussian assumption when designing second order blind estimation methods in the context of digital communications the low and high signal to noise ratio snr asymptotic performance of the maximum likelihood estimator derived assuming gaussian transmitted symbols is compared with the performance of the optimal second order estimator which exploits the actual distribution of the discrete constellation the asymptotic study concludes that the gaussian assumption leads to the optimal second order solution if the snr is very low or if the symbols belong to a multilevel constellation such as quadrature amplitude modulation qam or amplitude phase shift keying apsk on the other hand the gaussian assumption can yield important losses at high snr if the transmitted symbols are drawn from a constant modulus constellation such as phase shift keying psk or continuous phase modulations cpm these conclusions are illustrated for the problem of direction of arrival doa estimation of multiple digitally modulated signals

asymptotic properties of the algebraic constant modulus algorithm the algebraic constant modulus algorithm acma is a noniterative blind source separation algorithm it computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem we analyze its asymptotic properties and show that unlike cma it converges to the wiener beamformer when the number of samples or the signal to noise ratio snr goes to infinity we also sketch its connection to the related jade algorithm and derive a version of acma that converges to a zero forcing beamformer this gives improved performance in applications that use the estimated mixing matrix such as in direction finding

the procrustes problem for orthogonal kronecker products the procrustes problem for orthogonal kronecker products is considered given matrices a in mathbb r n 2 times k 2 t in mathbb r n 2 times n 2 n ge k we minimize the frobenius norm t q otimes q a for all orthogonal stiefel matrices q in mathbb r n times k q tq i k we introduce and implement left and right relaxation methods for minimization numerical results for the data in general and kronecker product form are given to illustrate convergence of both methods

a least squares approach to blind beamforming we present a least squares approach to blind beamforming two estimation criteria are derived one turns out to coincide with the comon 1990 1994 criterion whereas the other is a computationally simpler criterion similar in nature to the joint approximate diagonalization of eigenmatrices jade criterion

asymptotic distributions of maximum likelihood tests for change in the mean summary for the maximum likelihood tests for a change in the mean of independent random variables it is proved that the limit distribution is a double exponential i e gumbel distribution our results also explain why the maximum likelihood methods are very powerful on the tails the time intervals between explosions in british coal mines between 1851 and 1950 in which more than ten people were killed were analysed by maguire pearson wynn 1952 who concluded that the data had an exponential distribution with constant mean over time cox lewis 1966 ch 3 jarrett 1979 and worsley 1986 have reanalysed the data and found strong evidence that the mean did not remain constant hsu 1979 found a significant change in stock market returns by analysing the weekly closing values of the dow jones industrial average from july 1 1971 to august 2 1974 cobb 1978 examined measurements of the annual volume of discharge from the nile river at aswan for the years 1871 to 1970 and found a decrease in annual volume after 1898

bounded component analysis of noisy underdetermined and overdetermined mixtures our paper addresses the problem of the bounded component analysis of the observations in noisy mixtures we present an original set of assumptions that guarantee the identifiability of the mixture in underdetermined mixtures and the separability of the sources in overdetermined mixtures these assumptions are especially well suited for the blind identification of communication channels our proof of the identifiability of the mixing system is non constructive thus we develop a novel blind identification criterion for underdetermined and overdetermined mixtures which is based on the least square fit of the perimeter of a set of projections of the observations for the optimization of this criterion we propose the bca pm algorithm which implements the natural gradient descent along with an acceleration of the convergence designed for the neighbourhood of the solution in situations of isotropic gaussian noise and for reasonable signal to noise ratio bca pm compares favorably with respect to other state of the art methods such as the ica simultaneous diagonalization algorithms for underdetermined and overdetermined mixtures the simulations also corroborate the advantages of using bounded component analysis for the blind identification of the channel with small datasets or when the transmitters cannot be regarded as statistically independent

an improved bonferroni inequality and applications summary we present an improved bonferroni inequality which gives an upper bound for the probability of the union of an arbitrary sequence of events the bound is constructed in terms of the joint probability of pairs of events which are represented by edges on a graph examples of applications to periodicity location shift detection kolmogorovsmirnov tests and outlier detection are given

change point estimation using nonparametric regression we consider a regression model in which the mean function may have a discontinuity at an unknown point we propose an estimate of the location of the discontinuity based on one side nonparametric regression estimates of the mean function the change point estimate is shown to converge in probability at rate 0 n 1 and to have the same asymptotic distribution as maximum likelihood estimates considered by other authors under parametric regression models confidence regions for the location and size of the change are also discussed

tests for a change point summary the problem considered is that of testing a sequence of independent normal random variables with constant known or unknown variance for no change in mean versus alternatives with a single change point various tests such as those based on the likelihood ratio and recursive residuals are studied power approximations are developed by integrating approximations for conditional boundary crossing probabilities a comparison of several tests is made

an analytical constant modulus algorithm iterative constant modulus algorithms such as godard 1980 and cma have been used to blindly separate a superposition of cochannel constant modulus cm signals impinging on an antenna array these algorithms have certain deficiencies in the context of convergence to local minima and the retrieval of all individual cm signals that are present in the channel we show that the underlying constant modulus factorization problem is in fact a generalized eigenvalue problem and may be solved via a simultaneous diagonalization of a set of matrices with this new analytical approach it is possible to detect the number of cm signals present in the channel and to retrieve all of them exactly rejecting other non cm signals only a modest amount of samples is required the algorithm is robust in the presence of noise and is tested on measured data collected from an experimental set up

misadjustment and tracking analysis of the constant modulus array the constant modulus cm array is a blind adaptive beamformer that can separate cochannel signals a follow on adaptive signal canceler may be used to perform direction finding of the source captured by the array in this paper we analyze the convergence and tracking properties of the cm array using a least mean square approximation expressions are derived for the misadjustment of the adaptive algorithms and a tracking model is developed that accurately predicts the behavior of the system during fades it is demonstrated that the adaptive canceler contributes more to the overall misadjustment than does the adaptive cm beamformer computer simulations are presented to illustrate the transient properties of the system and to verify the analytical results

the power of likelihood ratio and cumulative sum tests for a change in a binomial probability summary a simple iterative procedure is found for the exact null and alternative distributions of likelihood ratio cumulative sum and related statistics for testing for a change in probability of a sequence of independent binomial random variables it is concluded that the likelihood ratio test is slightly less powerful than the cumulative sum test near the middle of the sequence but that the likelihood ratio test is much more powerful near the ends an example from epidemiology is used to illustrate the results under certain conditions the cumulative sum test is identical to a two sample kolmogorov smirnov test and so the iterative procedure can be used to find the null distribution of kolmogorov smirnov and related statistics

direction of arrival estimation for constant modulus signals in many cases where direction finding is of interest the signals impinging on an antenna array are known to be phase modulated and hence to have a constant modulus cm this is a strong property by itself it is already sufficient for source separation and can be used to construct improved direction finding algorithms we first derive the relevant cramer rao bounds crbs for arbitrary array configurations and specialize to uniform linear arrays we then propose a simple suboptimal direction estimation algorithm in which the signals are separated using the cm property followed by direction finding on the decoupled signals compared with the esprit algorithm and the crb for arbitrary signals the algorithm shows good results

tests for parameter instability and structural change with unknown change point this paper considers tests for parameter instability and structural change with unknown change point the results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures the asymptotic distributions of the test statistics considered here are nonstandard because the change point parameter only appears under the alternative hypothesis and not under the null the tests considered here are shown to have nontrivial asymptotic local power against all alternatives for which the parameters are nonconstant the tests are found to perform quite well in a monte carlo experiment reported elsewhere copyright 1993 by the econometric society

essential self adjointness for semi bounded magnetic schrodinger operators on non compact manifolds abstract we prove essential self adjointness for semi bounded below magnetic schrodinger operators on complete riemannian manifolds with a given positive smooth measure which is fixed independently of the metric some singularities of the scalar potential are allowed this is an extension of the povzner wienholtz simader theorem the proof uses the scheme of wienholtz but requires a refined invariant integration by parts technique as well as the use of a family of cut off functions which are constructed by a non trivial smoothing procedure due to karcher

multiple changepoint fitting via quasilikelihood with application to dna sequence segmentation summary we consider situations where a step function with a variable number of steps provides an adequate model for a regression relationship while the variance of the observations depends on their mean this model provides for discontinuous jumps at changepoints and for constant means and error variances in between changepoints the basic statistical problem consists of identification of the number of changepoints their locations and the levels the function assumes in between we embed this problem into a quasilikelihood formulation and utilise the minimum deviance criterion to fit the model for the choice of the number of changepoints we discuss a modified schwarz criterion a dynamic programming algorithm makes the segmentation feasible for sequences of moderate length the performance of the segmentation method is demonstrated in an application to the segmentation of the bacteriophage l sequence

blind source separation the location of local minima in the case of finitely many samples cost functions used in blind source separation are often defined in terms of expectations i e an infinite number of samples is assumed an open question is whether the local minima of finite sample approximations to such cost functions are close to the minima in the infinite sample case to answer this question we develop a new methodology of analyzing the finite sample behavior of general blind source separation cost functions in particular we derive a new probabilistic analysis of the rate of convergence as a function of the number of samples and the conditioning of the mixing matrix the method gives a connection between the number of available samples and the probability of obtaining a local minimum of the finite sample approximation within a given sphere around the local minimum of the infinite sample cost function this shows the convergence in probability of the nearest local minima of the finite sample approximation to the local minima of the infinite sample cost function we also answer a long standing problem of the mean squared error mse behavior of the finite sample least squares constant modulus algorithm ls cma namely whether there exist ls cma receivers with good mse performance we demonstrate how the proposed techniques can be used to determine the required number of samples for ls cma to exceed a specified performance the paper concludes with simulations that validate the results

the spectrum of the schrodinger operator on decrit le spectre negatif de l operateur de schrodinger avec un potentiel singulier on determine la valeur exacte du fond du spectre et on etablit une double estimation

relationships between the constant modulus and wiener receivers the godard 1980 or the constant modulus algorithm cma is an effective technique for blind receiver design in communications however due to the complexity of the constant modulus cm cost function the performance of the cm receivers has primarily been evaluated using simulations theoretical analysis is typically based on either the noiseless case or approximations of the cost function the following question while resolvable numerically for a specific example remains unanswered in a generic manner in the presence of channel noise where are the cm local minima and what are their mean squared errors mse in this paper a geometrical approach is presented that relates the cm to wiener or minimum mse receivers given the mse and the intersymbol user interference of a wiener receiver a sufficient condition is given for the existence of a cm local minimum in the neighborhood of the wiener receiver the mse bounds on cm receiver performance are derived and shown to be tight in simulations the analysis shows that while in some cases the cm receiver performs almost as well as the nonblind wiener receiver it is also possible that due to its blind nature the cm receiver may perform considerably worse than a nonblind wiener receiver

the minimum risk principle that underlies the criteria of bounded component analysis this paper studies the problem of the blind extraction of a subset of bounded component signals from the observations of a linear mixture in the first part of this paper we analyze the geometric assumptions of the observations that characterize the problem and their implications on the mixing matrix and latent sources in the second part we solve the problem by adopting the principle of minimizing the risk which refers to the encoding complexity of the observations in the worst admissible situation this principle provides an underlying justification of several bounded component analysis bca criteria including the minimum normalized volume criterion of the estimated sources or the maximum negentropy likelihood criterion with a uniform reference model for the estimated sources this unifying framework can explain the differences between the criteria in accordance with their considered hypotheses for the model of the observations this paper is first presented for the case of the extraction of a complex and multidimensional source and later is particularized for the case of the extraction of subsets of 1 d complex sources the results also hold true in the case of real signals where the obtained criteria for the extraction of a set of 1 d sources usually coincide with the existing bca criteria

conservative confidence bands in curvilinear regression this paper gives a method for constructing conservative scheffe type simultaneous confidence bands for curvilinear regression functions over finite intervals the method is based on the use of a geometric inequality giving an upper bound for the uniform measure of the set of points within a given distance from y an arbitrary piecewise differentiable path with finite length in s k 1 the unit sphere in r k the upper bound is obtained by straightening the path so that it lies in a great circle in s k 1

kruskal s permutation lemma and the identification of candecomp parafac and bilinear models with constant modulus constraints candecomp parafac cp analysis is an extension of low rank matrix decomposition to higher way arrays which are also referred to as tensors cp extends and unifies several array signal processing tools and has found applications ranging from multidimensional harmonic retrieval and angle carrier estimation to blind multiuser detection the uniqueness of cp decomposition is not fully understood yet despite its theoretical and practical significance toward this end we first revisit kruskal s permutation lemma which is a cornerstone result in the area using an accessible basic linear algebra and induction approach the new proof highlights the nature and limits of the identification process we then derive two equivalent necessary and sufficient uniqueness conditions for the case where one of the component matrices involved in the decomposition is full column rank these new conditions explain a curious example provided recently in a previous paper by sidiropoulos who showed that kruskal s condition is in general sufficient but not necessary for uniqueness and that uniqueness depends on the particular joint pattern of zeros in the possibly pretransformed component matrices as another interesting application of the permutation lemma we derive a similar necessary and sufficient condition for unique bilinear factorization under constant modulus cm constraints thus providing an interesting link to and unification with cp

statistical performance analysis of the algebraic constant modulus algorithm this paper presents a large sample analysis of the covariance of the beamformers computed by the analytical constant modulus algorithm acma method for blindly separating constant modulus sources this can be used to predict the signal to interference plus noise ratio sinr performance of these beamformers as well as their deviation from the nonblind wiener receivers to which they asymptotically converge the analysis is based on viewing acma as a subspace fitting optimization where the subspace is spanned by the eigenvectors of a fourth order covariance matrix the theoretical performance is illustrated by numerical simulations and shows a good match

decoupling of deficiency indices and applications to schrodinger type operators with possibly strongly singular potentials abstract we investigate closed symmetric l 2 r n realizations h of schrodinger type operators v c 0 r n whose potential coefficient v has a countable number of well separated singularities on compact sets j j j of n dimensional lebesgue measure zero with j n an index set and j j j we show that the defect def h of h can be computed in terms of the individual defects def h j of closed symmetric l 2 r n realizations of v j c 0 r n j with potential coefficient v j localized around the singularity j j j where v j j v j in particular we prove def h j j def h j including the possibility that one and hence both sides equal we first develop an abstract approach to the question of decoupling of deficiency indices and then apply it to the concrete case of schrodinger type operators in l 2 r n moreover we also show how operator and form bounds for v relative to h 0 h 2 r n can be estimated in terms of the operator and form bounds of v j j j relative to h 0 again we first prove an abstract result and then show its applicability to schrodinger type operators in l 2 r n extensions to second order locally uniformly elliptic differential operators on r n with a possibly strongly singular potential coefficient are treated as well

sequential changepoint detection in quality control and dynamical systems after a brief survey of a large variety of sequential detection procedures that are widely scattered in statistical references on quality control and engineering references on fault detection and signal processing we study some open problems concerning these procedures and introduce a unified theory of sequential changepoint detection this theory leads to a class of sequential detection rules which are not too demanding in computational and memory requirements for on line implementation and yet are nearly optimal under several performance criteria

on blind beamforming for multiple non gaussian signals and the constant modulus algorithm a new method for blindly separating multiple cochannel non gaussian signals received by a sensor array is presented the method is based on a cumulant based least squares criterion that for identically distributed negative kurtosis signals is proven to be identical to the 2 2 constant modulus cm cost function commonly used by cm algorithms a computationally simple algorithm is proposed to minimize the criterion the algorithm performs well even when the number of samples is small thus allowing its application in dynamic environments e g moving emitters for the special case of two signals only the minimization is obtained analytically simulation results are included

the distribution and moments of the smallest eigenvalue of a random matrix of wishart type abstract given a random rectangular m n matrix with elements from a normal distribution what is the distribution of the smallest singular value to pose an equivalent question in the language of multivariate statistics what is the distribution of the smallest eigenvalue of a matrix from the central wishart distribution in the null case we give new results giving the distribution as a simple recursion this includes the more difficult case when n m is an even integer without resorting to zonal polynomials and hypergeometric functions of matrix arguments with the recursion one can obtain exact expressions for the density and the moments of the distribution in terms of functions usually no more complicated than polynomials exponentials and at worst ordinary hypergeometric functions we further elaborate on the special cases when n m 0 1 2 and 3 and give a numerical table of the expected values for 2 m 25 and 0 n m 25

nonparametric change point estimation consider a sequence of independent random variables x i 1 leq i leq n having cdf f for i leq theta n and cdf g otherwise a class of strongly consistent estimators for the change point theta in 0 1 is proposed the estimators require no knowledge of the functional forms or parametric families of f and g furthermore f and g need not differ in their means or other measure of location the only requirement is that f and g differ on a set of positive probability the proof of consistency provides rates of convergence and bounds on the error probability for the estimators the estimators are applied to two well known data sets in both cases yielding results in close agreement with previous parametric analyses a simulation study is conducted showing that the estimators perform well even when f and g share the same mean variance and skewness

a low complexity blind estimator of narrowband polynomial phase signals consider the problem of estimating the parameters of multiple polynomial phase signals observed by a sensor array in practice it is difficult to maintain a precisely calibrated array the array manifold is then assumed to be unknown and the estimation is referred to as blind estimation to date only an approximated maximum likelihood estimator amle was suggested for blindly estimating the polynomial coefficients of each signal however this estimator requires a multidimensional search over the entire coefficient space instead we propose an estimation approach which is based on two steps first the signals are separated using a blind source separation technique which exploits the constant modulus property of the signals then the coefficients of each polynomial are estimated using a least squares method applied to the unwrapped phase of the estimated signal this estimator does not involve any search in the coefficient spaces the computational complexity of the proposed estimator increases linearly with respect to the polynomial order whereas that of the amle increases exponentially simulation results show that the proposed estimator achieves the cramer rao lower bound at moderate or high signal to noise ratio

analytical method for blind binary signal separation the blind separation of multiple co channel binary digital signals using an antenna array involves finding a factorization of a data matrix x into x as where all entries of s are 1 or 1 it is shown that this problem can be solved exactly and noniteratively via a certain generalized eigenvalue decomposition as indicated by simulations the algorithm is robust in the presence of noise an interesting implication is that certain cluster segmentation problems can be solved using eigenvalue techniques

on the essential self adjointness of schrodinger operators with locally integrable potentials results by simader brezis and cycon of the genre locally essentially self adjoint implies globally essentially self adjoint are generalised to schrodinger operators that are not necessarily bounded from below

an analysis of constant modulus receivers this paper investigates connections between non blind wiener receivers and blind receivers designed by minimizing the constant modulus cm cost applicable to both t spaced and fractionally spaced fir equalization the main results include 1 a test for the existence of cm local minima near wiener receivers 2 an analytical description of cm receivers in the neighborhood of wiener receivers 3 mean square error mse bounds for cm receivers when the channel matrix is invertible we also show that the cm receiver is approximately colinear with the wiener receiver and provide a quantitative measure of the size of neighborhoods that contain the cm receivers and the accuracy of the mse bounds

maximum likelihood estimation the cramer rao bound and the method of scoring with parameter constraints maximum likelihood ml estimation is a popular approach to solving many signal processing problems many of these problems cannot be solved analytically and so numerical techniques such as the method of scoring are applied however in many scenarios it is desirable to modify the ml problem with the inclusion of additional side information often this side information is in the form of parametric constraints which the ml estimate mle must now satisfy we unify the asymptotic constrained ml cml theory with the constrained cramer rao bound ccrb theory by showing the cml estimate cmle is asymptotically efficient with respect to the ccrb we also generalize the classical method of scoring using the ccrb to include the constraints satisfying the constraints after each iterate convergence properties and examples verify the usefulness of the constrained scoring approach as a particular example an alternative and more general cmle is developed for the complex parameter linear model with linear constraints a novel proof of the efficiency of this estimator is provided using the ccrb

new processing techniques based on the constant modulus adaptive algorithm this paper presents three extensions of the constant modulus algorithm cma introduced in an earlier paper as a means of correcting degradations in constant enyelope waveforms as originally formulated the cma employs an fir filter with complex coefficients and accepts complex quadrature input data in this paper first a real input real coefficient version of the algorithm is shown to perform arbitrarily close to the fully complex version secondly the algorithm is extended for the enhancement of signals having a nonconstant but known envelope as might arise in data signals with pulse shaping lastly a multichannel version of cma wherein several observations are linearly combined is presented for joint adaptation of multiple filters this approach can be used for example as a means of spatial or polarization beamsteering to reject additive interferers and compensate for channel induced polarization rotation

geometrical characterizations of constant modulus receivers convergence properties of the constant modulus cm and the shalvi weinstein sw algorithms in the presence of noise remain largely unknown a new geometrical approach to the analysis of constant modulus and shalvi weinstein receivers is proposed by considering a special constrained optimization involving norms of the combined channel receiver response this approach provides a unified framework within which various blind and nonblind wiener receivers can all be analyzed by circumscribing an ellipsoid by norm balls of different types a necessary and sufficient condition for the equivalence among constant modulus shalvi weinstein zero forcing and wiener receivers are obtained answers to open questions with regard to cm and sw receivers including their locations and their relationship with wiener receivers are provided for the special orthogonal channel and the general two dimensional 2 d channel receiver impulse response it is also shown that in two dimensions each cm or sw receiver is associated with one and only one wiener receiver

bounded component analysis of linear mixtures a criterion of minimum convex perimeter this study presents a blind and geometric technique which pursues the linear decomposition of the observations in bounded component signals the bounded component analysis of the observations relies on the hypotheses of compactness and cartesian decomposition of the convex support of the vector of component signals and in the invertibility of the mixture assumptions which in absence of noise are able to guarantee the identifiability of the mixture and separability of the components up to permutation scaling and phase ambiguities under these conditions the convex perimeter of the normalized linear combination of the observations is shown to be a global contrast function whose minima correspond with the extraction of bounded components of the observations practical extraction and separation algorithms based on the minimization of this criterion are given the experimental results with communications signals serve to illustrate the good performance of the proposed method in high snr scenarios even for a small number of samples

a global solution to the schrodinger equation from henstock to feynman one of the key elements of feynman s formulation of non relativistic quantum mechanics is a so called feynman path integral it plays an important role in the theory but it appears as a postulate based on intuition rather than a well defined object all previous attempts to supply feynman s theory with rigorous mathematics underpinning based on the physical requirements have not been satisfactory the difficulty comes from the need to define a measure on the infinite dimensional space of paths and to create an integral that would possess all of the properties requested by feynman in the present paper we consider a new approach to defining the feynman path integral based on the theory developed by muldowney a modern theory of random variable with applications in stochastic calcolus financial mathematics and feynman integration john wiley sons inc new jersey 2012 muldowney uses the henstock integration technique and deals with non absolute integrability of the fresnel integrals in orde

a stochastic differential equation model for the height growth of forest stands summary a brief review of the uses and methods of height growth prediction in forestry is given in 2 in 3 the proposed growth model is presented it consists of a stochastic differential equation related to the bertalanffy richards growth model and a measurement error com ponent explicit expressions and an efficient computational procedure for the likelihood function are obtained in 4 a method for the simultaneous maximum likelihood estimation of global and local parameters is outlined the log likelihood function is maximized by a modified newton method the special structure of the problem is exploited in order to handle the very large number of variables involved in the optimization the approach presented has been success fully implemented and some computational experience is reported in 5

statistical method for detecting structural change in the growth process due to competition among individual trees and other exogenous factors that change the growth environment each tree grows following its own growth trend with some structural changes in growth over time in the present article a new method is proposed to detect a structural change in the growth process we formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change to evaluate the p value of the test the tube method is developed because the regular distribution theory is insufficient using two sets of tree diameter growth data sampled from planted forest stands of cryptomeria japonica in japan we conduct an analysis of identifying the effect of thinning on the growth process as a structural change our results demonstrate that the proposed method is useful to identify the structural change caused by thinning we also provide the properties of the method in terms of the size and power of the test

estimation in forest yield models using composite link functions with random effects a sigmoidal forest yield model is estimated from longitudinal measurements of sample plots where measurement times and intervals vary the projection form of the yield model is fitted as a generalized linear model glm with composite link functions and an offset this allows age and additional covariates to be easily incorporated in a more flexible nonlinear structure than that provided by single link glms also existing algorithms for fitting generalized linear mixed models glmms are easily adapted to fit the projection model thereby allowing incorporation of random plot effects estimation for the composite link glmm is described as well as an alternative to the glmm in which random effects are incorporated as linear components on the same scale as the response this model is referred to as an additive generalized linear mixed model aglmm unlike the glmm the fixed effect parameter estimates in the aglmm are population average pa the aglmm is fitted using a modification of a subject specific ss algorithm using marginal expectation the random effects in the aglmm may have a natural interpretation or may provide a means of generating a marginal covariance structure as is the case with linear mixed models a simple example of an aglmm for binomial data and logit link is given in both this and the forest yield example a single artificial random effect is constructed to give exchangeable correlation for observations within subjects and a comparison of fit of the aglmm and glmm is made using a conditional quasi deviance if the fit is similar and pa parameter estimates are required the aglmm is preferred

fast subspace decomposition many recent signal processing techniques in various areas e g array signal processing system identification and spectrum estimation require a step of extracting the low dimensional subspace from a large space this step is usually called subspace decomposition which is conventionally accomplished by an eigendecomposition ed since an ed requires o m sup 3 flops for an m spl times m matrix it may represent a barrier to the real time implementation of these algorithms the authors present a fast algorithm for signal subspace decomposition which is termed fsd that exploits the special matrix structure low rank plus identity associated with signal subspace algorithms and requires only o m sup 2 d flops where d often spl lt m denotes the signal subspace dimension unlike most of alternatives the dimension of the signal subspace is not assumed known apriori and is estimated as part of the procedure moreover theoretical analysis has been conducted and its results show the strong consistency of the new detection scheme and the asymptotic equivalence between fsd and ed in estimating the signal subspace their new approach can also exploit other matrix structure common in signal processing areas e g toeplitz or hankel and thus achieve another order of computational reduction moreover it can be easily implemented in parallel to reduce further the computation time to as little as o md using o m simple processors