Imputation. AT&T shall impute to its costs of providing telecommunications services (and charge any affiliate, subsidiary, or associate company engaged in the provision of such services) an equal amount to the charges set forth in this Section for all of the Conduits, Ducts, and Poles it occupies and uses.
Imputation. BellSouth shall impute to its costs of providing telecommunications services (and charge any affiliate, subsidiary, or associate company engaged in the provision of such services) an equal amount to the charges set forth in this Section for all of the conduits, ducts, and poles it occupies and uses.
Imputation. The knowledge or actions or failure to act on the part of any fiduciary of the Company shall not be imputed to Indemnitee, Indemnitee’s Spouse or any Controlling Person for purposes of determining entitlement to indemnification under this Agreement.
Imputation. All sums of money received by the Agent and the Secured Parties from the Borrower or from anyone whomsoever, which may be imputed in reduction of the Guaranteed Obligations, shall be considered as payments which the Agent and the Secured Parties may impute, subject to the provisions of the Transaction Documents, as they see fit, notwithstanding the provisions of Article 1572 of the Civil Code of Quebec as well as every other legal rule concerning the imputation of payments.
Imputation. Toute somme pouvant être reçue par l’un ou l’autre des Créanciers et provenant de la réalisation ou de la liquidation d’actifs affectés par les Sûretés xxxxx être imputée ou distribuée selon l’ordre de collocation prévu à l’article 3.1 et de façon à xxxxxx effet aux dispositions de la présente convention.
Imputation. Imputation is the function of the justice of God directed toward mankind and relates to the plan of God for mankind. Imputation functions as an act of condemnation or blessing from the integrity of God to mankind. Imputation is the action of the justice of God whereby either condemnation or blessing is assigned, ascribed, attributed or superimposed to another being. The believer advances in the plan of God with each imputation.
Imputation. BellSouth shall impute to its costs of providing telecommunications services (and charge any affiliate, subsidiary, or associate company engaged in the provision of such services) an equal amount to the charges set forth in this Attachment for all of the conduits, ducts, and poles it occupies and uses.
Imputation. To expand the number of interrogated SNVs, imputation can be used for technologies that do not cover the entire genome (Table 2.2). Imputation is predominantly used in GWAS analysis and to expand the PGx panel in genome wide arrays [8]. With imputation, the presence of a genomic variant is inferred based on the absence or presence of a linked SNV. These predictions all come with a probability for the occurrence of the SNV of interest. Often only imputations with a high probability are included (e.g., >90%) to avoid inaccurate assignments. Xxxxxxxx et al. have shown that imputation accuracies as high as 99% can be reached for PGx variants [8]. Nevertheless, a probability of 90% also means that there is a 10% change that the imputed variant is not correct. While this is certainly acceptable for population studies, it is not sufficient for tailoring drug treatment in an individual patient. Furthermore, to reach high imputation accuracy, an imputation dataset specific to each patient’s ethnical background is needed as the level of linkage disequilibrium (LD) between two SNVs can differ between different populations [8,75]. One clear example of the differences in LD between populations is the HLA tagging SNVs; to identify the HLA-A*3101 allele, associated with carbamazepine toxicity, a linked SNV is used. In Caucasians, the rs1061235 (NC_000006.12:g.29945521A>T) variant is in full LD with the *3101 haplotype, therefore the presence of the HLA-A*3101 allele can be inferred based on the presence of the rs1061235 variant [76]. However, in the Asian population, this variant is not in LD with the *3101 allele. For individuals of Asian descent, the rs1633021 (NC_000006.12:g.29779092T>C) variant can be used as a linked SNV as this variant is in LD with HLA-A*3101 in this population [77]. Using the Caucasian-linked SNV in the wrong population can lead to errors in the inferred haplotype, phenotype and ultimately lead to treatment errors. Therefore, the application of imputation should be limited to research purposes until the reliability for an individual patient has been proven.
Imputation. We simulate data using information from our actual data. For all subjects N=250 we simulate data for Y1, X0, X0, and Y4 to represent ADS from week one through week four. Given the characteristics of our data we find that our data fits MAR. We will utilize information from our drop-out analysis and select one factor related to drop-out. We will extend our assumption, for the purposes of our simulation, that this variable is also related to not reporting ADS. Then we will vary the amount of missingness utilizing this fact. Consider marital status as the variable that we will utilize. In addition to Yi values each subject will also have marital status. If married women are more likely to not report ADS we will give them a higher probability of missing data than the other participants. We will accomplish this by randomly generating a number from the uniform distribution from (0, 1) for each observation. If we would like 10% missing observations getting a value from 0 to 0.10 will be deleted. We will vary the amount of missingness (10%, 15%, 20%, and 25%) compare the effectiveness of different imputation methods Avg, Prev, Post, LaN, LVCF, and NVCB utilizing bias and proportionate variance. bias = ∑(y − yˆ ) PV = var(yˆ) var(y) We define yˆ as the imputed value, y is is the actual value, and m is the number of missing values. A positive bias indicates underestimated imputed values. PV is the proportionate variance. It is a ratio of the observed variance to the true various used to assess under dispersion. When the PV=1 the variance of the imputed values is equal to the true values. A PV value of less than one indicates underestimation. We compute these values for each participant and find the mean across all participants.
Imputation. We compare the following simple imputation methods: Avg, Prev, Post, LVCF, NVCB, and LaN. To revisit the composition, we simulate data for N=250 subjects, allowing 30% to represent married women, with the following assumptions as denoted in the table below: Y1 7987.68 213.86 Y2 8325.02 208.04 Y3 8444.55 260.46 Y4 8076.51 217.25 We simulate missing data using three scenarios, being influenced by the results from the drop-out analysis:
1. Married Women experiencing 20% missing data, Other participants 10%
2. Married Women experiencing 30% missing data, Other participants 15%
3. Married Women experiencing 40% missing data, Other participants 20% Table 13 displays the results for the first scenario. Given the large values that we start with we find that the bias for all methods is not extremely high. Most values underestimate Y1-Y4. The NVCB imputation method experiences the largest underestimation and overestimation. LaN slightly underestimates values, however, it experiences the smallest bias. We experience PV close to 1 for all methods for selected measures, which means the variance of our imputed values are close to the variance of our original data.
