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gradient of the negative log-likelihood function  (SAS institute)


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    SAS institute gradient of the negative log-likelihood function
    Gradient Of The Negative Log Likelihood Function, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Article Title: A modelling framework for analysing the reproductive output of individual plants grown in monoculture
    Article Snippet: We maximised the log of the likelihood function using the NLMIXED procedure using the software SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) to obtain estimates of all parameters of the two component models defined by (3) and (8).

    Article Title: Slope estimation for informatively right censored longitudinal data modelling the number of observations using geometric and Poisson distributions: application to renal transplant cohort.
    Article Snippet: Analysis of longitudinal data is often complicated by the presence of informative right censoring.. This type of censoring should be accounted for in the analysis so that valid slope estimates are attained.. In this study, we developed a new likelihood-based approach wherein the likelihood function is integrated over random effects to obtain a marginal likelihood function.

    Article Title: Characterisation of polymorphic microsatellite markers from Aegilops tauschii and transferability to the D-genome of bread wheat.
    Article Snippet: Microsatellites were isolated from a Aegilops tauschii (the D-genome donor of bread wheat) library enriched for various motifs.. Primers generated from the flanking region of the microsatellites were used successfully to amplify the corresponding loci in the D genome of bread wheat.. Additional amplification sometimes also occurred from the A and B genomes.

    Article Title: Joint models of dynamics of mothers’ stress and children’s disease
    Article Snippet: We propose two types of joint two-state continuous time Markov models using shared random effect(s).. A simulation study is conducted to evaluate the performance of the parameter estimations under various scenarios and to compare each type of joint model approach to the separate model approach.. The proposed method is applied to the Mothers’ Stress and Children’s Morbidity study.



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    Whole set of α ^ estimated. On the x -axis, α is the true value of the exponents, the values computed in the generator. On the y -axis, the estimated values of α ^ computed by the corresponding analysis method are located. Red curves represent true alpha values α , and blue curves represent the estimated alpha values α ^ . The first column (A,C,E) corresponds to the signals generated via the Cholesky method, and the second column (B,D,F) corresponds to the signals generated via ARFIMA filtering. The first row (A,B) presents the α ^ values computed using fGn-based Whittle’s maximum likelihood estimator, the second row (C,D) presents those computed using ARFIMA-based Whittle’s maximum likelihood estimator, and the third row (E,F) presents the α ^ values computed using DFA.

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Whole set of α ^ estimated. On the x -axis, α is the true value of the exponents, the values computed in the generator. On the y -axis, the estimated values of α ^ computed by the corresponding analysis method are located. Red curves represent true alpha values α , and blue curves represent the estimated alpha values α ^ . The first column (A,C,E) corresponds to the signals generated via the Cholesky method, and the second column (B,D,F) corresponds to the signals generated via ARFIMA filtering. The first row (A,B) presents the α ^ values computed using fGn-based Whittle’s maximum likelihood estimator, the second row (C,D) presents those computed using ARFIMA-based Whittle’s maximum likelihood estimator, and the third row (E,F) presents the α ^ values computed using DFA.

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques: Generated

    Periodogram of choleskyfgn (A) , arfima0d0 (B) , whitenoise (C) , and empirical (D) signals with the theoretical power spectral density of fGn (orange curve) and ARFIMA (0, d ,0) (yellow curve). The theoretical power spectral densities were computed with the estimated values of H and d obtained via whittle.m. Those values, entered in MATLAB code 2 and 3, are presented in .

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Periodogram of choleskyfgn (A) , arfima0d0 (B) , whitenoise (C) , and empirical (D) signals with the theoretical power spectral density of fGn (orange curve) and ARFIMA (0, d ,0) (yellow curve). The theoretical power spectral densities were computed with the estimated values of H and d obtained via whittle.m. Those values, entered in MATLAB code 2 and 3, are presented in .

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques:

    Periodogram of choleskyfgn (A) , arfima0d0 (B) , whitenoise (C) , and empirical (D) signals (blue curve) with the adjusted theoretical power spectral density of fGn (orange curve) and ARFIMA (0, d ,0) (yellow curve). The H and d values are the same as those used in the previous figure.

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Periodogram of choleskyfgn (A) , arfima0d0 (B) , whitenoise (C) , and empirical (D) signals (blue curve) with the adjusted theoretical power spectral density of fGn (orange curve) and ARFIMA (0, d ,0) (yellow curve). The H and d values are the same as those used in the previous figure.

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques:

    Whittle’s log-likelihood functions of choleskyfgn (A) , arfima0d0 (B) , whitenoise (C) , and empirical (D) signals. The blue curves correspond to Whittle’s likelihood function calculated using the fGn theoretical spectrum, while the orange curves correspond to the same function calculated using the ARFIMA (0, d ,0) theoretical spectrum.

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Whittle’s log-likelihood functions of choleskyfgn (A) , arfima0d0 (B) , whitenoise (C) , and empirical (D) signals. The blue curves correspond to Whittle’s likelihood function calculated using the fGn theoretical spectrum, while the orange curves correspond to the same function calculated using the ARFIMA (0, d ,0) theoretical spectrum.

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques:

    Box plot of α ^ squared error values obtained via fGn-based Whittle’s likelihood (A) , ARFIMA-based Whittle’s likelihood (B) , and DFA (C) . The lower and upper edges of the boxes represent the 25 and 75 percentiles, respectively. The horizontal black line represents the median. The whiskers extend to the most extreme points not considered as outliers. The outliers are plotted as individual points. The orange diamond represents the MSE value.

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Box plot of α ^ squared error values obtained via fGn-based Whittle’s likelihood (A) , ARFIMA-based Whittle’s likelihood (B) , and DFA (C) . The lower and upper edges of the boxes represent the 25 and 75 percentiles, respectively. The horizontal black line represents the median. The whiskers extend to the most extreme points not considered as outliers. The outliers are plotted as individual points. The orange diamond represents the MSE value.

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques:

    Box plot of α ^ squared error values obtained using ARFIMA-based Whittle’s likelihood for four sets of length: 32 (A) , 64 (B) , 128 (C) , and 256 (D) . The lower and upper edges of the boxes represent the 25 and 75 percentiles, respectively. The horizontal black line represents the median. The whiskers extend to the most extreme points not considered as outliers. The outliers are plotted as individual points. The orange diamond represents the MSE value. The vertical scale of the top left graph is 20 times larger than the other three panels.

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Box plot of α ^ squared error values obtained using ARFIMA-based Whittle’s likelihood for four sets of length: 32 (A) , 64 (B) , 128 (C) , and 256 (D) . The lower and upper edges of the boxes represent the 25 and 75 percentiles, respectively. The horizontal black line represents the median. The whiskers extend to the most extreme points not considered as outliers. The outliers are plotted as individual points. The orange diamond represents the MSE value. The vertical scale of the top left graph is 20 times larger than the other three panels.

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques:

    Comparison of α ^ estimated via ARFIMA-based Whittle’s likelihood and DFA on gait data made available by J. Hausdorff on the PhysioNet platform ( <xref ref-type= Goldberger et al., 2000 ; Hausdorff, 2001 ). The results highlighted in red correspond to anti-persistent series, i.e., with α lower than 0.5." width="100%" height="100%">

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: Comparison of α ^ estimated via ARFIMA-based Whittle’s likelihood and DFA on gait data made available by J. Hausdorff on the PhysioNet platform ( Goldberger et al., 2000 ; Hausdorff, 2001 ). The results highlighted in red correspond to anti-persistent series, i.e., with α lower than 0.5.

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques: Comparison

    log-power spectral density of Hausdorff data rearranged into three groups: persistent behavior (A) , anti-persistent behavior (B) , and mixed behavior (C) . log-power spectral density of an artificial ARFIMA(p,d,q) signal with parameters (2, −0.35.1) generated using ARFIMApdq.m (D) .

    Journal: Frontiers in Network Physiology

    Article Title: A guide to Whittle maximum likelihood estimator in MATLAB

    doi: 10.3389/fnetp.2023.1204757

    Figure Lengend Snippet: log-power spectral density of Hausdorff data rearranged into three groups: persistent behavior (A) , anti-persistent behavior (B) , and mixed behavior (C) . log-power spectral density of an artificial ARFIMA(p,d,q) signal with parameters (2, −0.35.1) generated using ARFIMApdq.m (D) .

    Article Snippet: .................................................................. MATLAB code 6 : Whittle’s log-likelihood function with fGn theoretical PSD function lwHfgn = WLLFfgn (H,w,P,N) Tp = sin (pi*H)*gamma ((2*H)+1)*(abs(w).^(1-(2*H))); c = sum(P)/sum (Tp); T = c *Tp; lwHfgn=(2/N)*sum (log(T)+(P./T)); .................................................................. MATLAB code 7 : Whittle’s log-likelihood function with ARFIMA (0,d,0) theoretical PSD function lwHarf = WLLFarf (H,w,P,N) d = H-0.5; Tp=(1/(2*pi))*(2*sin (w/2)).^-(2*d); c = sum(P)/sum (Tp); T = c *Tp; lwHarf=(2/N)*sum (log(T)+(P./T)); .................................................................. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf .

    Techniques: Generated